---
title: "A Survey of Deep Learning for Geometry Problem Solving (2026)"
description: "This survey maps how deep learning is used to solve geometry problems. 331 references, cited by 1 paper here."
authors: ["Jianzhe Ma", "Wenxuan Wang", "Qin Jin"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.1829/"
doi: "10.18653/v1/2026.acl-long.1829"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/ma-2026-survey-deep-learning-geometry-problem/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/ma-2026-survey-deep-learning-geometry-problem/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/9/) / A Survey of Deep Learning for Geometry Problem Solving

# A Survey of Deep Learning for Geometry Problem Solving

**Authors:** [Jianzhe Ma](https://md.research.elamur.ai/free/en/authors/jianzhe-ma/), [Wenxuan Wang](https://md.research.elamur.ai/free/en/authors/wenxuan-wang/) and [Qin Jin](https://md.research.elamur.ai/free/en/authors/qin-jin/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.1829/) · [PDF](https://aclanthology.org/2026.acl-long.1829.pdf) · [DOI 10.18653/v1/2026.acl-long.1829](https://doi.org/10.18653/v1/2026.acl-long.1829) · [OpenAlex](https://openalex.org/W7166832905)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · [1](https://md.research.elamur.ai/free/en/papers/ma-2026-survey-deep-learning-geometry-problem/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
This survey maps how deep learning is used to solve geometry problems. The task is a long-standing test of machine mathematical reasoning and a natural multimodal benchmark, because solving a problem means reading a diagram, reasoning logically and computing numbers. The authors began from classic papers, ran one round of forward and backward citation snowballing, searched Google Scholar, and screened the results by hand, ending with over 310 relevant papers.

They sort the field into three task levels. Fundamental skills include diagram understanding, text parsing, relation extraction and knowledge prediction. The core tasks are theorem proving and numerical calculation. Geometry also appears inside broader math-reasoning benchmarks. Methods are grouped by architecture, training stage and inference stage. The paper also reviews automatic and manual evaluation, compares representative models, and names open problems.

The survey's value is that it organizes a fast-growing, previously scattered subfield. It also offers a maintained paper list and a research agenda.

## Key points
- Publications on deep learning for geometry problem solving grew from 1 in 2018 to 112 in 2024. The paper list is hosted at github.com/majianz/dl4gps.
- Example datasets by task:
  - numerical calculation: GEOS, GeoQA, Geometry3K, PGPS9K
  - proving: UniGeo, IMO-AG-30, MO-TG-225
  - diagram understanding: PGDP5K, AutoGeo
  - composite math reasoning: MATH, MathVista, MathVerse
- The dominant architecture is the Encoder-Decoder. Its parts are a text encoder (LSTM, GRU or Transformer), a diagram encoder (CNN, ViT or VQ-VAE), a fusion module, a decoder and an optional knowledge module. GANs, GNNs and decoder-only models are also reviewed.
- Training approaches covered are pre-training, supervised fine-tuning and reinforcement learning. Inference approaches covered are test-time scaling and knowledge-augmented methods.
- Proposed future directions:
  - tighter coupling of perception and reasoning
  - geometry-specific RL, such as process reward models (PRMs) and rewards from symbolic provers or verifiers
  - strategies inspired by human problem solving
  - dedicated geometry-education systems

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Renmin University of China](https://md.research.elamur.ai/free/en/universities/renmin-university-of-china/) — University, Beijing, China · 114 works · 16,118 citations

📚 Shelves (5): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [➗ Mathematics](https://md.research.elamur.ai/free/en/shelves/mathematics/) · 177 works · [🧮 Computational Theory and Mathematics](https://md.research.elamur.ai/free/en/shelves/computer-science/computational-theory-and-mathematics/) · 252 works · [➗ Applied Mathematics](https://md.research.elamur.ai/free/en/shelves/mathematics/applied-mathematics/) · 46 works · [➗ Geometry and Topology](https://md.research.elamur.ai/free/en/shelves/mathematics/geometry-and-topology/) · 3 works

🧭 Topics (1): [🧮 Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/) · 1,426 works

## Cite

Cite as: Jianzhe Ma, Wenxuan Wang and Qin Jin. 2026. A Survey of Deep Learning for Geometry Problem Solving. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{ma2026survey,
  title = {{A Survey of Deep Learning for Geometry Problem Solving}},
  author = {Jianzhe Ma, Wenxuan Wang and Qin Jin},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1829},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1829}
}
```

- [← previous A Self-Reflective LLM-based Architecture for Semi-Open Event Extraction](https://md.research.elamur.ai/free/en/papers/tanev-2026-self-reflective-llm-based-architecture/)
- [next → A Survey on LLM-based Conversational User Simulation](https://md.research.elamur.ai/free/en/papers/ni-2026-survey-llm-based-conversational-user/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/9/)

## References

331 references, 62 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Archana Boob and Mansi A Radke. 2024b. 2d shape detection for solving geometry word problems. IETE Journal of Research, 70(6):5617–5632. [find](https://scholar.google.com/scholar?q=Archana+Boob+and+Mansi+A+Radke%2E+2024b%2E+2d+shape+detection+for+solving+geometry+word+problems%2E+IETE+Journal+of+Research%2C+70%286%29%3A5617%E2%80%935632%2E)
2. Vansh Agrawal, Pratham Singla, Amitoj Singh Miglani, Shivank Garg, and Ayush Mangal. 2024. [Give me a hint: Can llms take a hint to solve math problems?](https://md.research.elamur.ai/free/en/papers/agrawal-2024-give-me-hint-llms-take/) In The 4th Workshop on Mathematical Reasoning and AI at NeurIPS’24.
3. Janice Ahn, Rishu Verma, Renze Lou, Di Liu, Rui Zhang, and Wenpeng Yin. 2024. [Large language models for mathematical reasoning: Progresses and challenges](https://md.research.elamur.ai/free/en/papers/ahn-2024-large-language-models-mathematical-reasoning/). In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop, pages…
4. Chris Alvin, Sumit Gulwani, Rupak Majumdar, and Supratik Mukhopadhyay. 2017. Synthesis of solutions for shaded area geometry problems. In FLAIRS, pages 14–19. [find](https://scholar.google.com/scholar?q=Chris+Alvin%2C+Sumit+Gulwani%2C+Rupak+Majumdar%2C+and+Supratik+Mukhopadhyay%2E+2017%2E+Synthesis+of+solutions+for+shaded+area+geometry+problems%2E+In+FLAIRS%2C+pages+14%E2%80%9319%2E)
5. Avinash Anand, Raj Jaiswal, Abhishek Dharmadhikari, Atharva Marathe, Harsh Popat, Harshil Mital, Ashwin R Nair, Kritarth Prasad, Sidharth Kumar, Astha Verma, and 1 others. 2024a. Geovqa: A comprehensive multimodal geometry dataset for secondary education. In 2024 IEEE 7th International Conference on… [find](https://scholar.google.com/scholar?q=Avinash+Anand%2C+Raj+Jaiswal%2C+Abhishek+Dharmadhikari%2C+Atharva+Marathe%2C+Harsh+Popat%2C+Harshil+Mital%2C+Ashwin+R+Nair%2C+Kritarth+Prasad%2C+Sidharth+Kumar%2C+Astha+Verma%2C+and+1+others%2E+2024a%2E+Geovqa%3A+A+comprehensi)
6. Avinash Anand, Raj Jaiswal, Abhishek Dharmadhikari, Atharva Marathe, Harsh Parimal Popat, Harshil Mital, Kritarth Prasad, Rajiv Ratn Shah, and Roger Zimmermann. 2024b. Improving multimodal llms ability in geometry problem solving, reasoning, and multistep scoring. arXiv preprint arXiv:2412.00846. — [arXiv:2412.00846](https://arxiv.org/abs/2412.00846) [find](https://scholar.google.com/scholar?q=Avinash+Anand%2C+Raj+Jaiswal%2C+Abhishek+Dharmadhikari%2C+Atharva+Marathe%2C+Harsh+Parimal+Popat%2C+Harshil+Mital%2C+Kritarth+Prasad%2C+Rajiv+Ratn+Shah%2C+and+Roger+Zimmermann%2E+2024b%2E+Improving+multimodal+llms+abilit)
7. Archana Boob, Shiva Reddy, Deep Walke, Harshini Pillarisetti, Shreeya Shukla, and Mansi Radke. 2024. Automatic extraction of structured information from elementary level geometry questions into logic forms. Multimedia Tools and Applications, pages 1–25. [find](https://scholar.google.com/scholar?q=Archana+Boob%2C+Shiva+Reddy%2C+Deep+Walke%2C+Harshini+Pillarisetti%2C+Shreeya+Shukla%2C+and+Mansi+Radke%2E+2024%2E+Automatic+extraction+of+structured+information+from+elementary+level+geometry+questions+into+logic+)
8. Archana Narayandas Boob, Prajakta Dnyaneshwar Bo-dakhe, Mansi Anup Radke, and Umesh Ashok Deshpande. 2023. Extracting structured information from the textual description of geometry word problems. In Proceedings of the 2023 7th International Conference on Natural Language Processing and Information… [find](https://scholar.google.com/scholar?q=Archana+Narayandas+Boob%2C+Prajakta+Dnyaneshwar+Bo%2Ddakhe%2C+Mansi+Anup+Radke%2C+and+Umesh+Ashok+Deshpande%2E+2023%2E+Extracting+structured+information+from+the+textual+description+of+geometry+word+problems%2E+In+)
9. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, and 1 others. 2020. [Language models are few-shot learners](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/). Advances in neural information processing systems, 33:1877–1901.
10. Huanqia Cai, Yijun Yang, and Zhifeng Li. 2025. System-2 mathematical reasoning via enriched instruction tuning. Transactions on Machine Learning Research. [find](https://scholar.google.com/scholar?q=Huanqia+Cai%2C+Yijun+Yang%2C+and+Zhifeng+Li%2E+2025%2E+System%2D2+mathematical+reasoning+via+enriched+instruction+tuning%2E+Transactions+on+Machine+Learning+Research%2E)
11. Shihao Cai, Keqin Bao, Hangyu Guo, Jizhi Zhang, Jun Song, and Bo Zheng. 2024. Geogpt4v: Towards geometric multi-modal large language models with geometric image generation. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 750–766. [find](https://scholar.google.com/scholar?q=Shihao+Cai%2C+Keqin+Bao%2C+Hangyu+Guo%2C+Jizhi+Zhang%2C+Jun+Song%2C+and+Bo+Zheng%2E+2024%2E+Geogpt4v%3A+Towards+geometric+multi%2Dmodal+large+language+models+with+geometric+image+generation%2E+In+Proceedings+of+the+2024+)
12. Jie Cao and Jing Xiao. 2022. An augmented benchmark dataset for geometric question answering through dual parallel text encoding. In Proceedings of the 29th international conference on computational linguistics, pages 1511–1520. [find](https://scholar.google.com/scholar?q=Jie+Cao+and+Jing+Xiao%2E+2022%2E+An+augmented+benchmark+dataset+for+geometric+question+answering+through+dual+parallel+text+encoding%2E+In+Proceedings+of+the+29th+international+conference+on+computational+l)
13. Hyunsik Chae, Seungwoo Yoon, Chloe Yewon Chun, Gyehun Go, Yongin Cho, Gyeongmin Lee, and Ernest K Ryu. 2024. Decomposing complex visual comprehension into atomic visual skills for vision language models. In The 4th Workshop on Mathematical Reasoning and AI at NeurIPS’24. [find](https://scholar.google.com/scholar?q=Hyunsik+Chae%2C+Seungwoo+Yoon%2C+Chloe+Yewon+Chun%2C+Gyehun+Go%2C+Yongin+Cho%2C+Gyeongmin+Lee%2C+and+Ernest+K+Ryu%2E+2024%2E+Decomposing+complex+visual+comprehension+into+atomic+visual+skills+for+vision+language+mode)
14. Felix Chen, Hangjie Yuan, Yunqiu Xu, Tao Feng, Jun Cen, Pengwei Liu, Zeying Huang, and Yi Yang. 2025a. Mathflow: Enhancing the perceptual flow of mllms for visual mathematical problems. arXiv preprint arXiv:2503.16549. [find](https://scholar.google.com/scholar?q=Felix+Chen%2C+Hangjie+Yuan%2C+Yunqiu+Xu%2C+Tao+Feng%2C+Jun+Cen%2C+Pengwei+Liu%2C+Zeying+Huang%2C+and+Yi+Yang%2E+2025a%2E+Mathflow%3A+Enhancing+the+perceptual+flow+of+mllms+for+visual+mathematical+problems%2E+arXiv+preprint)
15. Hardy Chen, Haoqin Tu, Fali Wang, Hui Liu, Xianfeng Tang, Xinya Du, Yuyin Zhou, and Cihang Xie. 2025b. [Sft or rl? an early investigation into training r1-like reasoning large vision-language models](https://md.research.elamur.ai/free/en/papers/chen-2025-sft-rl-early-investigation-training/). Transactions on Machine Learning Research.
16. Jiaao Chen, Xiaoman Pan, Dian Yu, Kaiqiang Song, Xiaoyang Wang, Dong Yu, and Jianshu Chen. 2024a. Skills-in-context: Unlocking compositionality in large language models. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 13838–13890. [find](https://scholar.google.com/scholar?q=Jiaao+Chen%2C+Xiaoman+Pan%2C+Dian+Yu%2C+Kaiqiang+Song%2C+Xiaoyang+Wang%2C+Dong+Yu%2C+and+Jianshu+Chen%2E+2024a%2E+Skills%2Din%2Dcontext%3A+Unlocking+compositionality+in+large+language+models%2E+In+Findings+of+the+Association)
17. Jiaqi Chen, Tong Li, Jinghui Qin, Pan Lu, Liang Lin, Chongyu Chen, and Xiaodan Liang. 2022. [Unigeo: Unifying geometry logical reasoning via reformulating mathematical expression](https://md.research.elamur.ai/free/en/papers/chen-2022-unigeo-unifying-geometry-logical-reasoning/). In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 3313–3323.
18. Jiaqi Chen, Jianheng Tang, Jinghui Qin, Xiaodan Liang, Lingbo Liu, Eric Xing, and Liang Lin. 2021. [Geoqa: A geometric question answering benchmark towards multimodal numerical reasoning](https://md.research.elamur.ai/free/en/papers/chen-2021-geoqa-geometric-question-answering-benchmark/). In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 513–523.
19. Jingchang Chen, Hongxuan Tang, Zheng Chu, Qianglong Chen, Zekun Wang, Ming Liu, and Bing Qin. 2024b. Divide-and-conquer meets consensus: Unleashing the power of functions in code generation. In The Thirty-eighth Annual Conference on Neural Information Processing Systems. [find](https://scholar.google.com/scholar?q=Jingchang+Chen%2C+Hongxuan+Tang%2C+Zheng+Chu%2C+Qianglong+Chen%2C+Zekun+Wang%2C+Ming+Liu%2C+and+Bing+Qin%2E+2024b%2E+Divide%2Dand%2Dconquer+meets+consensus%3A+Unleashing+the+power+of+functions+in+code+generation%2E+In+The+Th)
20. Qiguang Chen, Libo Qin, Jin Zhang, Zhi Chen, Xiao Xu, and Wanxiang Che. 2024c. M3cot: A novel benchmark for multi-domain multi-step multi-modal chain-of-thought. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8199–8221. [find](https://scholar.google.com/scholar?q=Qiguang+Chen%2C+Libo+Qin%2C+Jin+Zhang%2C+Zhi+Chen%2C+Xiao+Xu%2C+and+Wanxiang+Che%2E+2024c%2E+M3cot%3A+A+novel+benchmark+for+multi%2Ddomain+multi%2Dstep+multi%2Dmodal+chain%2Dof%2Dthought%2E+In+Proceedings+of+the+62nd+Annual+Meet)
21. Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W Cohen. 2023a. Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks. Transactions on Machine Learning Research. [find](https://scholar.google.com/scholar?q=Wenhu+Chen%2C+Xueguang+Ma%2C+Xinyi+Wang%2C+and+William+W+Cohen%2E+2023a%2E+Program+of+thoughts+prompting%3A+Disentangling+computation+from+reasoning+for+numerical+reasoning+tasks%2E+Transactions+on+Machine+Learning)
22. Wenhu Chen, Ming Yin, Max Ku, Pan Lu, Yixin Wan, Xueguang Ma, Jianyu Xu, Xinyi Wang, and Tony Xia. 2023b. [Theoremqa: A theorem-driven question answering dataset](https://md.research.elamur.ai/free/en/papers/chen-2023-theoremqa-theorem-driven-question-answering/). In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 7889–7901.
23. Yan Chen, Xiaoqing Lu, Jingwei Qu, and Zhi Tang. 2016. Analysis of stroke intersection for overlapping pgf elements. In 2016 12th IAPR Workshop on Document Analysis Systems (DAS), pages 245–250. IEEE. [find](https://scholar.google.com/scholar?q=Yan+Chen%2C+Xiaoqing+Lu%2C+Jingwei+Qu%2C+and+Zhi+Tang%2E+2016%2E+Analysis+of+stroke+intersection+for+overlapping+pgf+elements%2E+In+2016+12th+IAPR+Workshop+on+Document+Analysis+Systems+%28DAS%29%2C+pages+245%E2%80%93250%2E+IEEE%2E)
24. Zhe Chen, Jiannan Wu, Wenhai Wang, Weijie Su, Guo Chen, Sen Xing, Muyan Zhong, Qinglong Zhang, Xizhou Zhu, Lewei Lu, and 1 others. 2024d. [Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks](https://md.research.elamur.ai/free/en/papers/chen-2023-internvl-scaling-up-vision/). In Proceedings of the IEEE/CVF conference on computer vision and p…
25. Jo-Ku Cheng, Zeren Zhang, Ran Chen, Jingyang Deng, Ziran Qin, and Jinwen Ma. 2025a. Geouni: A unified model for generating geometry diagrams, problems and problem solutions. In Proceedings of the 33rd ACM International Conference on Multimedia, pages 3057–3066. [find](https://scholar.google.com/scholar?q=Jo%2DKu+Cheng%2C+Zeren+Zhang%2C+Ran+Chen%2C+Jingyang+Deng%2C+Ziran+Qin%2C+and+Jinwen+Ma%2E+2025a%2E+Geouni%3A+A+unified+model+for+generating+geometry+diagrams%2C+problems+and+problem+solutions%2E+In+Proceedings+of+the+33rd)
26. Kanzhi Cheng, Li YanTao, Fangzhi Xu, Jianbing Zhang, Hao Zhou, and Yang Liu. 2025b. [Vision-language models can self-improve reasoning via reflection](https://md.research.elamur.ai/free/en/papers/cheng-2025-vision-language-models-can-self/). In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technolo…
27. Zihui Cheng, Qiguang Chen, Jin Zhang, Hao Fei, Xiaocheng Feng, Wanxiang Che, Min Li, and Libo Qin. 2025c. Comt: A novel benchmark for chain of multimodal thought on large vision-language models. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 23678–23686. [find](https://scholar.google.com/scholar?q=Zihui+Cheng%2C+Qiguang+Chen%2C+Jin+Zhang%2C+Hao+Fei%2C+Xiaocheng+Feng%2C+Wanxiang+Che%2C+Min+Li%2C+and+Libo+Qin%2E+2025c%2E+Comt%3A+A+novel+benchmark+for+chain+of+multimodal+thought+on+large+vision%2Dlanguage+models%2E+In+Pr)
28. Yuri Chervonyi, Trieu H Trinh, Miroslav Olšák, Xiaomeng Yang, Hoang H Nguyen, Marcelo Menegali, Junehyuk Jung, Junsu Kim, Vikas Verma, Quoc V Le, and 1 others. 2025. Gold-medalist performance in solving olympiad geometry with alphageometry2. Journal of Machine Learning Research, 26(241):1– 39. [find](https://scholar.google.com/scholar?q=Yuri+Chervonyi%2C+Trieu+H+Trinh%2C+Miroslav+Ol%C5%A1%C3%A1k%2C+Xiaomeng+Yang%2C+Hoang+H+Nguyen%2C+Marcelo+Menegali%2C+Junehyuk+Jung%2C+Junsu+Kim%2C+Vikas+Verma%2C+Quoc+V+Le%2C+and+1+others%2E+2025%2E+Gold%2Dmedalist+performance+in+solvi)
29. Kyunghyun Cho, Bart van Merriënboer, Çağlar G"ulçehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. [Learning phrase representations using rnn encoder–decoder for statistical machine translation](https://md.research.elamur.ai/free/en/papers/cho-2014-learning-phrase-representations-rnn-encoder/). In Proceedings of the 2014 Conference on Empirical Methods in Natural Lang…
30. Seunghyuk Cho, Zhenyue Qin, Yang Liu, Youngbin Choi, Seungbeom Lee, and Dongwoo Kim. 2025. GeoDANO: Geometric VLM with domain agnostic vision encoder. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 7836–7851, Suzhou, China. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Seunghyuk+Cho%2C+Zhenyue+Qin%2C+Yang+Liu%2C+Youngbin+Choi%2C+Seungbeom+Lee%2C+and+Dongwoo+Kim%2E+2025%2E+GeoDANO%3A+Geometric+VLM+with+domain+agnostic+vision+encoder%2E+In+Findings+of+the+Association+for+Computational+)
31. Seunghyuk Cho, Zhenyue Qin, Yang Liu, Youngbin Choi, Seungbeom Lee, and Dongwoo Kim. 2026. Plane geometry problem solving with multi-modal reasoning: A survey. In Findings of the Association for Computational Linguistics: EACL 2026, pages 110–131. [find](https://scholar.google.com/scholar?q=Seunghyuk+Cho%2C+Zhenyue+Qin%2C+Yang+Liu%2C+Youngbin+Choi%2C+Seungbeom+Lee%2C+and+Dongwoo+Kim%2E+2026%2E+Plane+geometry+problem+solving+with+multi%2Dmodal+reasoning%3A+A+survey%2E+In+Findings+of+the+Association+for+Compu)
32. Guanting Dong, Chenghao Zhang, Mengjie Deng, Yutao Zhu, Zhicheng Dou, and Ji-Rong Wen. 2025. [Progressive multimodal reasoning via active retrieval](https://md.research.elamur.ai/free/en/papers/dong-2025-progressive-multimodal-reasoning-active-retrieval/). In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3579–3602.
33. Xiangxiang Chu, Hailang Huang, Xiao Zhang, Fei Wei, and Yong Wang. 2026. GPG: A simple and strong reinforcement learning baseline for model reasoning. In The Fourteenth International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Xiangxiang+Chu%2C+Hailang+Huang%2C+Xiao+Zhang%2C+Fei+Wei%2C+and+Yong+Wang%2E+2026%2E+GPG%3A+A+simple+and+strong+reinforcement+learning+baseline+for+model+reasoning%2E+In+The+Fourteenth+International+Conference+on+Lea)
34. Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, and 1 others. 2021. [An image is worth 16x16 words: Transformers for image recognition at scale](https://md.research.elamur.ai/free/en/papers/dosovitskiy-2020-image-worth-16x16-words-transformers/). In International Conference on…
35. Rémi Coulom. 2006. Efficient selectivity and backup operators in monte-carlo tree search. In International conference on computers and games, pages 72–83. Springer. [find](https://scholar.google.com/scholar?q=R%C3%A9mi+Coulom%2E+2006%2E+Efficient+selectivity+and+backup+operators+in+monte%2Dcarlo+tree+search%2E+In+International+conference+on+computers+and+games%2C+pages+72%E2%80%9383%2E+Springer%2E)
36. DEBRUP DAS, Debopriyo Banerjee, Somak Aditya, and Ashish Kulkarni. 2024. Mathsensei: Mathematical reasoning with a tool-augmented large language model. In ICLR 2024 Workshop on Mathematical and Empirical Understanding of Foundation Models. [find](https://scholar.google.com/scholar?q=DEBRUP+DAS%2C+Debopriyo+Banerjee%2C+Somak+Aditya%2C+and+Ashish+Kulkarni%2E+2024%2E+Mathsensei%3A+Mathematical+reasoning+with+a+tool%2Daugmented+large+language+model%2E+In+ICLR+2024+Workshop+on+Mathematical+and+Empiri)
37. Arash Gholami Davoodi, Seyed Pouyan Mousavi Davoudi, and Pouya Pezeshkpour. 2025. LLMs are not intelligent thinkers: Introducing mathematical topic tree benchmark for comprehensive evaluation of LLMs. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for… [find](https://scholar.google.com/scholar?q=Arash+Gholami+Davoodi%2C+Seyed+Pouyan+Mousavi+Davoudi%2C+and+Pouya+Pezeshkpour%2E+2025%2E+LLMs+are+not+intelligent+thinkers%3A+Introducing+mathematical+topic+tree+benchmark+for+comprehensive+evaluation+of+LLMs%2E)
38. Huilin Deng, Ding Zou, Xinghao Zhao, Rui Ma, Yanming Guo, Yang Cao, and Yu Kang. 2025a. Curr-reft: Overcoming training bottlenecks in small-scale vision-language models via curriculum reinforcement finetuning. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 12021–1203… [find](https://scholar.google.com/scholar?q=Huilin+Deng%2C+Ding+Zou%2C+Xinghao+Zhao%2C+Rui+Ma%2C+Yanming+Guo%2C+Yang+Cao%2C+and+Yu+Kang%2E+2025a%2E+Curr%2Dreft%3A+Overcoming+training+bottlenecks+in+small%2Dscale+vision%2Dlanguage+models+via+curriculum+reinforcement+fi)
39. Linger Deng, Yuliang Liu, Bohan Li, Dongliang Luo, Liang Wu, Chengquan Zhang, Pengyuan Lyu, Ziyang Zhang, Gang Zhang, Errui Ding, and 1 others. 2024. R-cot: Reverse chain-of-thought problem generation for geometric reasoning in large multimodal models. arXiv preprint arXiv:2410.17885. — [arXiv:2410.17885](https://arxiv.org/abs/2410.17885) [find](https://scholar.google.com/scholar?q=Linger+Deng%2C+Yuliang+Liu%2C+Bohan+Li%2C+Dongliang+Luo%2C+Liang+Wu%2C+Chengquan+Zhang%2C+Pengyuan+Lyu%2C+Ziyang+Zhang%2C+Gang+Zhang%2C+Errui+Ding%2C+and+1+others%2E+2024%2E+R%2Dcot%3A+Reverse+chain%2Dof%2Dthought+problem+generation)
40. Yihe Deng, Hritik Bansal, Fan Yin, Nanyun Peng, Wei Wang, and Kai-Wei Chang. 2025b. Openvlthinker: An early exploration to complex vision-language reasoning via iterative self-improvement. arXiv preprint arXiv:2503.17352. — [arXiv:2503.17352](https://arxiv.org/abs/2503.17352) [find](https://scholar.google.com/scholar?q=Yihe+Deng%2C+Hritik+Bansal%2C+Fan+Yin%2C+Nanyun+Peng%2C+Wei+Wang%2C+and+Kai%2DWei+Chang%2E+2025b%2E+Openvlthinker%3A+An+early+exploration+to+complex+vision%2Dlanguage+reasoning+via+iterative+self%2Dimprovement%2E+arXiv+prepr)
41. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. [Bert: Pre-training of deep bidirectional transformers for language understanding](https://md.research.elamur.ai/free/en/papers/devlin-2019-bert-pre-training-deep-bidirectional/). In Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies,…
42. Jinghao Dou, Xiaopan Lyu, Xinguo Yu, and Hao Wu. 2024. An enhanced relation-flow algorithm for solving number line problems. In 2024 International Conference on Intelligent Education and Intelligent Research (IEIR), pages 1–6. IEEE. [find](https://scholar.google.com/scholar?q=Jinghao+Dou%2C+Xiaopan+Lyu%2C+Xinguo+Yu%2C+and+Hao+Wu%2E+2024%2E+An+enhanced+relation%2Dflow+algorithm+for+solving+number+line+problems%2E+In+2024+International+Conference+on+Intelligent+Education+and+Intelligent+R)
43. Yifan Du, Zikang Liu, Yifan Li, Wayne Xin Zhao, Yuqi Huo, Bingning Wang, Weipeng Chen, Zheng Liu, Zhongyuan Wang, and Ji-Rong Wen. 2025. Virgo: A preliminary exploration on reproducing o1-like mllm. arXiv preprint arXiv:2501.01904. — [arXiv:2501.01904](https://arxiv.org/abs/2501.01904) [find](https://scholar.google.com/scholar?q=Yifan+Du%2C+Zikang+Liu%2C+Yifan+Li%2C+Wayne+Xin+Zhao%2C+Yuqi+Huo%2C+Bingning+Wang%2C+Weipeng+Chen%2C+Zheng+Liu%2C+Zhongyuan+Wang%2C+and+Ji%2DRong+Wen%2E+2025%2E+Virgo%3A+A+preliminary+exploration+on+reproducing+o1%2Dlike+mllm%2E+a)
44. Xiuliang Duan, Dating Tan, Liangda Fang, Yuyu Zhou, Chaobo He, Ziliang Chen, Lusheng Wu, Guanliang Chen, Zhiguo Gong, Weiqi Luo, and 1 others. 2024. Reason-and-execute prompting: Enhancing multimodal large language models for solving geometry questions. In Proceedings of the 32nd ACM International C… [find](https://scholar.google.com/scholar?q=Xiuliang+Duan%2C+Dating+Tan%2C+Liangda+Fang%2C+Yuyu+Zhou%2C+Chaobo+He%2C+Ziliang+Chen%2C+Lusheng+Wu%2C+Guanliang+Chen%2C+Zhiguo+Gong%2C+Weiqi+Luo%2C+and+1+others%2E+2024%2E+Reason%2Dand%2Dexecute+prompting%3A+Enhancing+multimodal+)
45. Anas El Korchi and Youssef Ghanou. 2020. 2d geometric shapes dataset–for machine learning and pattern recognition. Data in Brief, 32:106090. [find](https://scholar.google.com/scholar?q=Anas+El+Korchi+and+Youssef+Ghanou%2E+2020%2E+2d+geometric+shapes+dataset%E2%80%93for+machine+learning+and+pattern+recognition%2E+Data+in+Brief%2C+32%3A106090%2E)
46. Jeffrey L Elman. 1990. Finding structure in time. Cognitive science, 14(2):179–211. [find](https://scholar.google.com/scholar?q=Jeffrey+L+Elman%2E+1990%2E+Finding+structure+in+time%2E+Cognitive+science%2C+14%282%29%3A179%E2%80%93211%2E)
47. Meng Fang, Xiangpeng Wan, Fei Lu, Fei Xing, and Kai Zou. 2025. Mathodyssey: Benchmarking mathematical problem-solving skills in large language models using odyssey math data. Scientific data, 12(1):1392. [find](https://scholar.google.com/scholar?q=Meng+Fang%2C+Xiangpeng+Wan%2C+Fei+Lu%2C+Fei+Xing%2C+and+Kai+Zou%2E+2025%2E+Mathodyssey%3A+Benchmarking+mathematical+problem%2Dsolving+skills+in+large+language+models+using+odyssey+math+data%2E+Scientific+data%2C+12%281%29%3A13)
48. Daocheng Fu, Zijun Chen, Renqiu Xia, Qi Liu, Yuan Feng, Hongbin Zhou, Renrui Zhang, Shiyang Feng, Peng Gao, Junchi Yan, and 1 others. 2025. Trust-geogen: Scalable and formal-verified data engine for trustworthy multi-modal geometric problem solving. arXiv preprint arXiv:2504.15780. — [arXiv:2504.15780](https://arxiv.org/abs/2504.15780) [find](https://scholar.google.com/scholar?q=Daocheng+Fu%2C+Zijun+Chen%2C+Renqiu+Xia%2C+Qi+Liu%2C+Yuan+Feng%2C+Hongbin+Zhou%2C+Renrui+Zhang%2C+Shiyang+Feng%2C+Peng+Gao%2C+Junchi+Yan%2C+and+1+others%2E+2025%2E+Trust%2Dgeogen%3A+Scalable+and+formal%2Dverified+data+engine+for+t)
49. Hongguang Fu, Jingzhong Zhang, Xiuqin Zhong, Mingkai Zha, and Li Liu. 2019. Robot for mathematics college entrance examination. In Electronic Proceedings of the 24th Asian Technology Conference in Mathematics, Mathematics and Technology, LLC. [find](https://scholar.google.com/scholar?q=Hongguang+Fu%2C+Jingzhong+Zhang%2C+Xiuqin+Zhong%2C+Mingkai+Zha%2C+and+Li+Liu%2E+2019%2E+Robot+for+mathematics+college+entrance+examination%2E+In+Electronic+Proceedings+of+the+24th+Asian+Technology+Conference+in+Mat)
50. Jinlan Fu, Shenzhen Huangfu, Hang Yan, See-Kiong Ng, and Xipeng Qiu. 2024. Hint-before-solving prompting: Guiding llms to effectively utilize encoded knowledge. arXiv preprint arXiv:2402.14310. — [arXiv:2402.14310](https://arxiv.org/abs/2402.14310) [find](https://scholar.google.com/scholar?q=Jinlan+Fu%2C+Shenzhen+Huangfu%2C+Hang+Yan%2C+See%2DKiong+Ng%2C+and+Xipeng+Qiu%2E+2024%2E+Hint%2Dbefore%2Dsolving+prompting%3A+Guiding+llms+to+effectively+utilize+encoded+knowledge%2E+arXiv+preprint+arXiv%3A2402%2E14310%2E+%E2%80%94+%5BarX)
51. Wenbin Gan, Xinguo Yu, Sichao Lai, and Lei Xiang. 2016. Plane geometry diagram retrieval by using hierarchical searching strategy. In Proceedings of the International Conference on Internet Multimedia Computing and Service, pages 201–206. [find](https://scholar.google.com/scholar?q=Wenbin+Gan%2C+Xinguo+Yu%2C+Sichao+Lai%2C+and+Lei+Xiang%2E+2016%2E+Plane+geometry+diagram+retrieval+by+using+hierarchical+searching+strategy%2E+In+Proceedings+of+the+International+Conference+on+Internet+Multimedia)
52. Team GLM, Aohan Zeng, Bin Xu, Bowen Wang, Chenhui Zhang, Da Yin, Dan Zhang, Diego Rojas, Guanyu Feng, Hanlin Zhao, and 1 others. 2024. [Chatglm: A family of large language models from glm-130b to glm-4 all tools](https://md.research.elamur.ai/free/en/papers/glm-2024-chatglm-family-large-language-models/). arXiv preprint arXiv:2406.12793.
53. Wenbin Gan, Xinguo Yu, Chao Sun, Bin He, and Mingshu Wang. 2017. Understanding plane geometry problems by integrating relations extracted from text and diagram. In Image and Video Technology: 8th Pacific-Rim Symposium, PSIVT 2017, Wuhan, China, November 20-24, 2017, Revised Selected Papers 8, pages… [find](https://scholar.google.com/scholar?q=Wenbin+Gan%2C+Xinguo+Yu%2C+Chao+Sun%2C+Bin+He%2C+and+Mingshu+Wang%2E+2017%2E+Understanding+plane+geometry+problems+by+integrating+relations+extracted+from+text+and+diagram%2E+In+Image+and+Video+Technology%3A+8th+Paci)
54. Salwa Tabet Gonzalez, Stéphane Graham-Lengrand, Julien Narboux, and Natarajan Shankar. 2021. Semantic parsing of geometry statements using supervised machine learning on synthetic data. In NatFoM 2021-CICM Workshop. [find](https://scholar.google.com/scholar?q=Salwa+Tabet+Gonzalez%2C+St%C3%A9phane+Graham%2DLengrand%2C+Julien+Narboux%2C+and+Natarajan+Shankar%2E+2021%2E+Semantic+parsing+of+geometry+statements+using+supervised+machine+learning+on+synthetic+data%2E+In+NatFoM+2021)
55. Wenbin Gan, Xinguo Yu, and Mingshu Wang. 2019a. Automatic understanding and formalization of plane geometry proving problems in natural language: A supervised approach. International Journal on Artificial Intelligence Tools, 28(04):1940003. [find](https://scholar.google.com/scholar?q=Wenbin+Gan%2C+Xinguo+Yu%2C+and+Mingshu+Wang%2E+2019a%2E+Automatic+understanding+and+formalization+of+plane+geometry+proving+problems+in+natural+language%3A+A+supervised+approach%2E+International+Journal+on+Artifi)
56. Wenbin Gan, Xinguo Yu, Ting Zhang, and Mingshu Wang. 2019b. Automatically proving plane geometry theorems stated by text and diagram. International Journal of Pattern Recognition and Artificial Intelligence, 33(07):1940003. [find](https://scholar.google.com/scholar?q=Wenbin+Gan%2C+Xinguo+Yu%2C+Ting+Zhang%2C+and+Mingshu+Wang%2E+2019b%2E+Automatically+proving+plane+geometry+theorems+stated+by+text+and+diagram%2E+International+Journal+of+Pattern+Recognition+and+Artificial+Intell)
57. Bofei Gao, Feifan Song, Zhe Yang, Zefan Cai, Yibo Miao, Chenghao Ma, Shanghaoran Quan, Liang Chen, Qingxiu Dong, Runxin Xu, and 1 others. 2025a. [Omni-math: A universal olympiad level mathematic benchmark for large language models](https://md.research.elamur.ai/free/en/papers/gao-2024-omni-math-universal-olympiad-level/). In The Thirteenth International Conference on Learning Representation…
58. Chang Gao, Haiyun Jiang, Deng Cai, Shuming Shi, and Wai Lam. 2024. Strategyllm: Large language models as strategy generators, executors, optimizers, and evaluators for problem solving. Advances in Neural Information Processing Systems, 37:96797–96846. [find](https://scholar.google.com/scholar?q=Chang+Gao%2C+Haiyun+Jiang%2C+Deng+Cai%2C+Shuming+Shi%2C+and+Wai+Lam%2E+2024%2E+Strategyllm%3A+Large+language+models+as+strategy+generators%2C+executors%2C+optimizers%2C+and+evaluators+for+problem+solving%2E+Advances+in+Neu)
59. Jiahui Gao, Renjie Pi, Jipeng Zhang, Jiacheng Ye, Wanjun Zhong, Yufei Wang, Lanqing HONG, Jianhua Han, Hang Xu, Zhenguo Li, and Lingpeng Kong. 2025b. G-LLaVA: Solving geometric problem with multi-modal large language model. In The Thirteenth International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Jiahui+Gao%2C+Renjie+Pi%2C+Jipeng+Zhang%2C+Jiacheng+Ye%2C+Wanjun+Zhong%2C+Yufei+Wang%2C+Lanqing+HONG%2C+Jianhua+Han%2C+Hang+Xu%2C+Zhenguo+Li%2C+and+Lingpeng+Kong%2E+2025b%2E+G%2DLLaVA%3A+Solving+geometric+problem+with+multi%2Dmoda)
60. Xiao-Shan Gao and Qiang Lin. 2004. Mmp/geometer–a software package for automated geometric reasoning. In Automated Deduction in Geometry: 4th International Workshop, ADG 2002, Hagenberg Castle, Austria, September 4-6, 2002. Revised Papers 4, pages 44–66. Springer. [find](https://scholar.google.com/scholar?q=Xiao%2DShan+Gao+and+Qiang+Lin%2E+2004%2E+Mmp%2Fgeometer%E2%80%93a+software+package+for+automated+geometric+reasoning%2E+In+Automated+Deduction+in+Geometry%3A+4th+International+Workshop%2C+ADG+2002%2C+Hagenberg+Castle%2C+Austri)
61. Herbert Gelernter, James R Hansen, and Donald W Loveland. 1960. Empirical explorations of the geometry theorem machine. In Papers presented at the May 3-5, 1960, western joint IRE-AIEE-ACM computer conference, pages 143–149. [find](https://scholar.google.com/scholar?q=Herbert+Gelernter%2C+James+R+Hansen%2C+and+Donald+W+Loveland%2E+1960%2E+Empirical+explorations+of+the+geometry+theorem+machine%2E+In+Papers+presented+at+the+May+3%2D5%2C+1960%2C+western+joint+IRE%2DAIEE%2DACM+computer+co)
62. Ross Girshick. 2015. Fast r-cnn. In Proceedings of the IEEE international conference on computer vision, pages 1440–1448. [find](https://scholar.google.com/scholar?q=Ross+Girshick%2E+2015%2E+Fast+r%2Dcnn%2E+In+Proceedings+of+the+IEEE+international+conference+on+computer+vision%2C+pages+1440%E2%80%931448%2E)
63. Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. Advances in neural information processing systems, 27. [find](https://scholar.google.com/scholar?q=Ian+J+Goodfellow%2C+Jean+Pouget%2DAbadie%2C+Mehdi+Mirza%2C+Bing+Xu%2C+David+Warde%2DFarley%2C+Sherjil+Ozair%2C+Aaron+Courville%2C+and+Yoshua+Bengio%2E+2014%2E+Generative+adversarial+nets%2E+Advances+in+neural+information+pro)
64. Zhibin Gou, Zhihong Shao, Yeyun Gong, Yujiu Yang, Minlie Huang, Nan Duan, Weizhu Chen, and 1 others. 2024. [Tora: A tool-integrated reasoning agent for mathematical problem solving](https://md.research.elamur.ai/free/en/papers/gou-2023-tora-tool-integrated-reasoning-agent/). In The Twelfth International Conference on Learning Representations.
65. Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, and 1 others. 2024. [The llama 3 herd of models](https://md.research.elamur.ai/free/en/papers/grattafiori-2024-llama-3-herd-models/). arXiv preprint arXiv:2407.21783.
66. Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, and 1 others. 2025a. [Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning](https://md.research.elamur.ai/free/en/papers/deepseek-ai-2025-deepseek-r1-incentivizing-reasoning-capability/). arXiv preprint arXiv:2501.12948.
67. Fucheng Guo and Pengpeng Jian. 2022. A graph convolutional network feature learning framework for interpretable geometry problem solving. In 2022 International Conference on Intelligent Education and Intelligent Research (IEIR), pages 59–64. IEEE. [find](https://scholar.google.com/scholar?q=Fucheng+Guo+and+Pengpeng+Jian%2E+2022%2E+A+graph+convolutional+network+feature+learning+framework+for+interpretable+geometry+problem+solving%2E+In+2022+International+Conference+on+Intelligent+Education+and+)
68. Jiawei Guo, Tianyu Zheng, Yizhi Li, Yuelin Bai, Bo Li, Yubo Wang, King Zhu, Graham Neubig, Wenhu Chen, and Xiang Yue. 2025b. [Mammoth-vl: Eliciting multimodal reasoning with instruction tuning at scale](https://md.research.elamur.ai/free/en/papers/guo-2025-mammoth-vl-eliciting-multimodal-reasoning/). In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume…
69. Yiduo Guo, Yaobo Liang, Chenfei Wu, Wenshan Wu, Dongyan Zhao, and Nan Duan. 2024. Learning to plan by updating natural language. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 10062–10098. [find](https://scholar.google.com/scholar?q=Yiduo+Guo%2C+Yaobo+Liang%2C+Chenfei+Wu%2C+Wenshan+Wu%2C+Dongyan+Zhao%2C+and+Nan+Duan%2E+2024%2E+Learning+to+plan+by+updating+natural+language%2E+In+Findings+of+the+Association+for+Computational+Linguistics%3A+EMNLP+202)
70. Xiaotian Han, Yiren Jian, Xuefeng Hu, Haogeng Liu, Yiqi Wang, Qihang Fan, Yuang Ai, Huaibo Huang, Ran He, Zhenheng Yang, and 1 others. 2024. Infimm-webmath-40b: Advancing multimodal pre-training for enhanced mathematical reasoning. In The 4th Workshop on Mathematical Reasoning and AI at NeurIPS’24. [find](https://scholar.google.com/scholar?q=Xiaotian+Han%2C+Yiren+Jian%2C+Xuefeng+Hu%2C+Haogeng+Liu%2C+Yiqi+Wang%2C+Qihang+Fan%2C+Yuang+Ai%2C+Huaibo+Huang%2C+Ran+He%2C+Zhenheng+Yang%2C+and+1+others%2E+2024%2E+Infimm%2Dwebmath%2D40b%3A+Advancing+multimodal+pre%2Dtraining+for+e)
71. Yihan Hao, Mingliang Zhang, Fei Yin, and Lin-Lin Huang. 2022. Pgdp5k: A diagram parsing dataset for plane geometry problems. In 2022 26th international conference on pattern recognition (ICPR), pages 1763–1769. IEEE. [find](https://scholar.google.com/scholar?q=Yihan+Hao%2C+Mingliang+Zhang%2C+Fei+Yin%2C+and+Lin%2DLin+Huang%2E+2022%2E+Pgdp5k%3A+A+diagram+parsing+dataset+for+plane+geometry+problems%2E+In+2022+26th+international+conference+on+pattern+recognition+%28ICPR%29%2C+pages+)
72. Chaoqun He, Renjie Luo, Yuzhuo Bai, Shengding Hu, Zhen Thai, Junhao Shen, Jinyi Hu, Xu Han, Yujie Huang, Yuxiang Zhang, and 1 others. 2024a. [Olympiadbench: A challenging benchmark for promoting agi with olympiad-level bilingual multimodal scientific problems](https://md.research.elamur.ai/free/en/papers/he-2024-olympiadbench-challenging-benchmark-promoting-agi/). In Proceedings of the 62nd Annual Meetin…
73. Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. 2022. [Masked autoencoders are scalable vision learners](https://md.research.elamur.ai/free/en/papers/he-2021-masked-autoencoders-scalable-vision-learners/). In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 16000–16009.
74. Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. 2017. Mask r-cnn. In Proceedings of the IEEE international conference on computer vision, pages 2961–2969. [find](https://scholar.google.com/scholar?q=Kaiming+He%2C+Georgia+Gkioxari%2C+Piotr+Doll%C3%A1r%2C+and+Ross+Girshick%2E+2017%2E+Mask+r%2Dcnn%2E+In+Proceedings+of+the+IEEE+international+conference+on+computer+vision%2C+pages+2961%E2%80%932969%2E)
75. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. [Deep residual learning for image recognition](https://md.research.elamur.ai/free/en/papers/he-2015-deep-residual-learning-image-recognition/). In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770– 778.
76. Yiming He, Jia Zou, Xiaokai Zhang, Na Zhu, and Tuo Leng. 2024b. Fgeo-tp: A language model-enhanced solver for euclidean geometry problems. Symmetry, 16(4):421. [find](https://scholar.google.com/scholar?q=Yiming+He%2C+Jia+Zou%2C+Xiaokai+Zhang%2C+Na+Zhu%2C+and+Tuo+Leng%2E+2024b%2E+Fgeo%2Dtp%3A+A+language+model%2Denhanced+solver+for+euclidean+geometry+problems%2E+Symmetry%2C+16%284%29%3A421%2E)
77. Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. 2021. [Measuring mathematical problem solving with the math dataset](https://md.research.elamur.ai/free/en/papers/hendrycks-2021-measuring-mathematical-problem-solving-math/). In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2).
78. Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation, 9(8):1735– 1780. [find](https://scholar.google.com/scholar?q=Sepp+Hochreiter+and+J%C3%BCrgen+Schmidhuber%2E+1997%2E+Long+short%2Dterm+memory%2E+Neural+computation%2C+9%288%29%3A1735%E2%80%93+1780%2E)
79. Mark Hopkins, Cristian Petrescu-Prahova, Roie Levin, Ronan Le Bras, Alvaro Herrasti, and Vidur Joshi. 2017. Beyond sentential semantic parsing: Tackling the math sat with a cascade of tree transducers. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 7… [find](https://scholar.google.com/scholar?q=Mark+Hopkins%2C+Cristian+Petrescu%2DPrahova%2C+Roie+Levin%2C+Ronan+Le+Bras%2C+Alvaro+Herrasti%2C+and+Vidur+Joshi%2E+2017%2E+Beyond+sentential+semantic+parsing%3A+Tackling+the+math+sat+with+a+cascade+of+tree+transducers)
80. Yushi Hu, Weijia Shi, Xingyu Fu, Dan Roth, Mari Ostendorf, Luke Zettlemoyer, Noah A Smith, and Ranjay Krishna. 2024. Visual sketchpad: Sketching as a visual chain of thought for multimodal language models. In NeurIPS 2024 Workshop on Behavioral Machine Learning. [find](https://scholar.google.com/scholar?q=Yushi+Hu%2C+Weijia+Shi%2C+Xingyu+Fu%2C+Dan+Roth%2C+Mari+Ostendorf%2C+Luke+Zettlemoyer%2C+Noah+A+Smith%2C+and+Ranjay+Krishna%2E+2024%2E+Visual+sketchpad%3A+Sketching+as+a+visual+chain+of+thought+for+multimodal+language+mo)
81. Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. 2017. Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4700–4708. [find](https://scholar.google.com/scholar?q=Gao+Huang%2C+Zhuang+Liu%2C+Laurens+Van+Der+Maaten%2C+and+Kilian+Q+Weinberger%2E+2017%2E+Densely+connected+convolutional+networks%2E+In+Proceedings+of+the+IEEE+conference+on+computer+vision+and+pattern+recognition)
82. Kaixuan Huang, Jiacheng Guo, Zihao Li, Xiang Ji, Jiawei Ge, Wenzhe Li, Yingqing Guo, Tianle Cai, Hui Yuan, Runzhe Wang, Yue Wu, Ming Yin, Shange Tang, Yangsibo Huang, Chi Jin, Xinyun Chen, Chiyuan Zhang, and Mengdi Wang. 2025a. MATH-perturb: Benchmarking LLMs’ math reasoning abilities against hard p… [find](https://scholar.google.com/scholar?q=Kaixuan+Huang%2C+Jiacheng+Guo%2C+Zihao+Li%2C+Xiang+Ji%2C+Jiawei+Ge%2C+Wenzhe+Li%2C+Yingqing+Guo%2C+Tianle+Cai%2C+Hui+Yuan%2C+Runzhe+Wang%2C+Yue+Wu%2C+Ming+Yin%2C+Shange+Tang%2C+Yangsibo+Huang%2C+Chi+Jin%2C+Xinyun+Chen%2C+Chiyuan+Zha)
83. Kung-Hsiang Huang, Can Qin, Haoyi Qiu, Philippe Laban, Shafiq Joty, Caiming Xiong, and Chien-Sheng Wu. 2025b. Why vision language models struggle with visual arithmetic? towards enhanced chart and geometry understanding. In Findings of the Association for Computational Linguistics: ACL 2025. [find](https://scholar.google.com/scholar?q=Kung%2DHsiang+Huang%2C+Can+Qin%2C+Haoyi+Qiu%2C+Philippe+Laban%2C+Shafiq+Joty%2C+Caiming+Xiong%2C+and+Chien%2DSheng+Wu%2E+2025b%2E+Why+vision+language+models+struggle+with+visual+arithmetic%3F+towards+enhanced+chart+and+geo)
84. Litian Huang, Xinguo Yu, and Bin He. 2022. A novel geometry problem understanding method based on uniform vectorized syntax-semantics model. In 2022 International Conference on Intelligent Education and Intelligent Research (IEIR), pages 78–85. IEEE. [find](https://scholar.google.com/scholar?q=Litian+Huang%2C+Xinguo+Yu%2C+and+Bin+He%2E+2022%2E+A+novel+geometry+problem+understanding+method+based+on+uniform+vectorized+syntax%2Dsemantics+model%2E+In+2022+International+Conference+on+Intelligent+Education+a)
85. Litian Huang, Xinguo Yu, Lei Niu, and Zihan Feng. 2023. Solving algebraic problems with geometry diagrams using syntax-semantics diagram understanding. Computers, Materials & Continua, 77(1). [find](https://scholar.google.com/scholar?q=Litian+Huang%2C+Xinguo+Yu%2C+Lei+Niu%2C+and+Zihan+Feng%2E+2023%2E+Solving+algebraic+problems+with+geometry+diagrams+using+syntax%2Dsemantics+diagram+understanding%2E+Computers%2C+Materials+%26+Continua%2C+77%281%29%2E)
86. Litian Huang, Xinguo Yu, Feng Xiong, Bin He, Sheng-bing Tang, and Jiawen Fu. 2024. Hologram reasoning for solving algebra problems with geometry diagrams. arXiv preprint arXiv:2408.10592. — [arXiv:2408.10592](https://arxiv.org/abs/2408.10592) [find](https://scholar.google.com/scholar?q=Litian+Huang%2C+Xinguo+Yu%2C+Feng+Xiong%2C+Bin+He%2C+Sheng%2Dbing+Tang%2C+and+Jiawen+Fu%2E+2024%2E+Hologram+reasoning+for+solving+algebra+problems+with+geometry+diagrams%2E+arXiv+preprint+arXiv%3A2408%2E10592%2E+%E2%80%94+%5BarXiv%3A240)
87. Qihan Huang, Weilong Dai, Jinlong Liu, Wanggui He, Hao Jiang, Mingli Song, Jingyuan Chen, Chang Yao, and Jie Song. 2025c. Boosting mllm reasoning with text-debiased hint-grpo. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4848–4857. [find](https://scholar.google.com/scholar?q=Qihan+Huang%2C+Weilong+Dai%2C+Jinlong+Liu%2C+Wanggui+He%2C+Hao+Jiang%2C+Mingli+Song%2C+Jingyuan+Chen%2C+Chang+Yao%2C+and+Jie+Song%2E+2025c%2E+Boosting+mllm+reasoning+with+text%2Ddebiased+hint%2Dgrpo%2E+In+Proceedings+of+the+IE)
88. Joy Hsu, Jiajun Wu, and Noah Goodman. 2022. Geoclidean: Few-shot generalization in euclidean geometry. Advances in Neural Information Processing Systems, 35:39007–39019. [find](https://scholar.google.com/scholar?q=Joy+Hsu%2C+Jiajun+Wu%2C+and+Noah+Goodman%2E+2022%2E+Geoclidean%3A+Few%2Dshot+generalization+in+euclidean+geometry%2E+Advances+in+Neural+Information+Processing+Systems%2C+35%3A39007%E2%80%9339019%2E)
89. Wenxuan Huang, Bohan Jia, Shaosheng Cao, Zheyu Ye, Fei zhao, Zhe Xu, Yao Hu, and Shaohui Lin. 2026. Vision-r1: Incentivizing reasoning capability in multimodal large language models. In The Fourteenth International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Wenxuan+Huang%2C+Bohan+Jia%2C+Shaosheng+Cao%2C+Zheyu+Ye%2C+Fei+zhao%2C+Zhe+Xu%2C+Yao+Hu%2C+and+Shaohui+Lin%2E+2026%2E+Vision%2Dr1%3A+Incentivizing+reasoning+capability+in+multimodal+large+language+models%2E+In+The+Fourteenth)
90. Pengfei Hu, Zhenrong Zhang, Qikai Chang, Shuhang Liu, Jiefeng Ma, Jun Du, Jianshu Zhang, Quan Liu, Jianqing Gao, Feng Ma, and 1 others. 2025. Prm-bas: Enhancing multimodal reasoning through Yiming Huang, Xiao Liu, Yeyun Gong, Zhibin Gou, Yelong Shen, Nan Duan, and Weizhu Chen. 2025d. Key-point-drive… [find](https://scholar.google.com/scholar?q=Pengfei+Hu%2C+Zhenrong+Zhang%2C+Qikai+Chang%2C+Shuhang+Liu%2C+Jiefeng+Ma%2C+Jun+Du%2C+Jianshu+Zhang%2C+Quan+Liu%2C+Jianqing+Gao%2C+Feng+Ma%2C+and+1+others%2E+2025%2E+Prm%2Dbas%3A+Enhancing+multimodal+reasoning+through+Yiming+Hua)
91. Zihan Huang, Tao Wu, Wang Lin, Shengyu Zhang, Jingyuan Chen, and Fei Wu. 2025e. Autogeo: Automating geometric image dataset creation for enhanced geometry understanding. IEEE Transactions on Multimedia. [find](https://scholar.google.com/scholar?q=Zihan+Huang%2C+Tao+Wu%2C+Wang+Lin%2C+Shengyu+Zhang%2C+Jingyuan+Chen%2C+and+Fei+Wu%2E+2025e%2E+Autogeo%3A+Automating+geometric+image+dataset+creation+for+enhanced+geometry+understanding%2E+IEEE+Transactions+on+Multimedi)
92. Anca-Elena Iordan. 2022. Usage of stacked long short-term memory for recognition of 3d analytic geometry elements. In ICAART (3), pages 745–752. [find](https://scholar.google.com/scholar?q=Anca%2DElena+Iordan%2E+2022%2E+Usage+of+stacked+long+short%2Dterm+memory+for+recognition+of+3d+analytic+geometry+elements%2E+In+ICAART+%283%29%2C+pages+745%E2%80%93752%2E)
93. Shachar Itzhaky, Sumit Gulwani, Neil Immerman, and Mooly Sagiv. 2013. Solving geometry problems using a combination of symbolic and numerical reasoning. In Logic for Programming, Artificial Intelligence, and Reasoning: 19th International Conference, LPAR-19, Stellenbosch, South Africa, December 14-1… [find](https://scholar.google.com/scholar?q=Shachar+Itzhaky%2C+Sumit+Gulwani%2C+Neil+Immerman%2C+and+Mooly+Sagiv%2E+2013%2E+Solving+geometry+problems+using+a+combination+of+symbolic+and+numerical+reasoning%2E+In+Logic+for+Programming%2C+Artificial+Intelligen)
94. Raj Jaiswal, Avinash Anand, and Rajiv Ratn Shah. 2024. Advancing multimodal llms: A focus on geometry problem solving reasoning and sequential scoring. In Proceedings of the 6th ACM International Conference on Multimedia in Asia, pages 1–7. [find](https://scholar.google.com/scholar?q=Raj+Jaiswal%2C+Avinash+Anand%2C+and+Rajiv+Ratn+Shah%2E+2024%2E+Advancing+multimodal+llms%3A+A+focus+on+geometry+problem+solving+reasoning+and+sequential+scoring%2E+In+Proceedings+of+the+6th+ACM+International+Conf)
95. Predrag Janičić and Julien Narboux. 2021. Automated generation of illustrations for synthetic geometry proofs. In Automated Deduction in Geometry, volume 352, pages 91–102. Open Publishing Association. [find](https://scholar.google.com/scholar?q=Predrag+Janic%CC%8Cic%CC%81+and+Julien+Narboux%2E+2021%2E+Automated+generation+of+illustrations+for+synthetic+geometry+proofs%2E+In+Automated+Deduction+in+Geometry%2C+volume+352%2C+pages+91%E2%80%93102%2E+Open+Publishing+Associati)
96. Ishadi Jayasinghe and Surangika Ranathunga. 2020. Two-step memory networks for deep semantic parsing of geometry word problems. In International Conference on Current Trends in Theory and Practice of Informatics, pages 676–685. Springer. [find](https://scholar.google.com/scholar?q=Ishadi+Jayasinghe+and+Surangika+Ranathunga%2E+2020%2E+Two%2Dstep+memory+networks+for+deep+semantic+parsing+of+geometry+word+problems%2E+In+International+Conference+on+Current+Trends+in+Theory+and+Practice+of+)
97. Mengzhao Jia, Zhihan Zhang, Wenhao Yu, Fangkai Jiao, and Meng Jiang. 2024. Describe-then-reason: Improving multimodal mathematical reasoning through visual comprehension training. arXiv preprint arXiv:2404.14604. — [arXiv:2404.14604](https://arxiv.org/abs/2404.14604) [find](https://scholar.google.com/scholar?q=Mengzhao+Jia%2C+Zhihan+Zhang%2C+Wenhao+Yu%2C+Fangkai+Jiao%2C+and+Meng+Jiang%2E+2024%2E+Describe%2Dthen%2Dreason%3A+Improving+multimodal+mathematical+reasoning+through+visual+comprehension+training%2E+arXiv+preprint+arXiv)
98. Yiming Jia, Jiachen Li, Xiang Yue, Bo Li, Ping Nie, Kai Zou, and Wenhu Chen. 2025. [Visualwebinstruct: Scaling up multimodal instruction data through web search](https://md.research.elamur.ai/free/en/papers/jia-2025-visualwebinstruct-scaling-up-multimodal-instruction/). In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 1373–1393.
99. Pengpeng Jian, Fucheng Guo, Cong Pan, Yanli Wang, Yangrui Yang, and Yang Li. 2023a. Interpretable geometry problem solving using improved retinanet and graph convolutional network. Electronics, 12(22):4578. [find](https://scholar.google.com/scholar?q=Pengpeng+Jian%2C+Fucheng+Guo%2C+Cong+Pan%2C+Yanli+Wang%2C+Yangrui+Yang%2C+and+Yang+Li%2E+2023a%2E+Interpretable+geometry+problem+solving+using+improved+retinanet+and+graph+convolutional+network%2E+Electronics%2C+12%2822%29)
100. Pengpeng Jian, Fucheng Guo, Yanli Wang, and Yang Li. 2023b. Solving geometry problems via feature learning and contrastive learning of multimodal data. CMES-Computer Modeling in Engineering & Sciences, 136(2). [find](https://scholar.google.com/scholar?q=Pengpeng+Jian%2C+Fucheng+Guo%2C+Yanli+Wang%2C+and+Yang+Li%2E+2023b%2E+Solving+geometry+problems+via+feature+learning+and+contrastive+learning+of+multimodal+data%2E+CMES%2DComputer+Modeling+in+Engineering+%26+Sciences)
101. Zhihuan Jiang, Zhen Yang, Jinhao Chen, Zhengxiao Du, Weihan Wang, Bin Xu, and Jie Tang. 2024. Visscience: An extensive benchmark for evaluating k12 educational multi-modal scientific reasoning. arXiv preprint arXiv:2409.13730. — [arXiv:2409.13730](https://arxiv.org/abs/2409.13730) [find](https://scholar.google.com/scholar?q=Zhihuan+Jiang%2C+Zhen+Yang%2C+Jinhao+Chen%2C+Zhengxiao+Du%2C+Weihan+Wang%2C+Bin+Xu%2C+and+Jie+Tang%2E+2024%2E+Visscience%3A+An+extensive+benchmark+for+evaluating+k12+educational+multi%2Dmodal+scientific+reasoning%2E+arXiv+)
102. Can Jin, Hongwu Peng, Qixin Zhang, Yujin Tang, Tong Che, and Dimitris N Metaxas. 2025. Two heads are better than one: Test-time scaling of multi-agent collaborative reasoning. In Workshop on Scaling Environments for Agents. [find](https://scholar.google.com/scholar?q=Can+Jin%2C+Hongwu+Peng%2C+Qixin+Zhang%2C+Yujin+Tang%2C+Tong+Che%2C+and+Dimitris+N+Metaxas%2E+2025%2E+Two+heads+are+better+than+one%3A+Test%2Dtime+scaling+of+multi%2Dagent+collaborative+reasoning%2E+In+Workshop+on+Scaling+E)
103. Vidur Joshi, Matthew E Peters, and Mark Hopkins. 2018. [Extending a parser to distant domains using a few dozen partially annotated examples](https://md.research.elamur.ai/free/en/papers/joshi-2018-extending-parser-distant-domains-few/). In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1190–1199.
104. Ryo Kamoi, Yusen Zhang, Sarkar Snigdha Sarathi Das, Ranran Haoran Zhang, and Rui Zhang. 2025. Vison-lyQA: Large vision language models still struggle with visual perception of geometric information. In Second Conference on Language Modeling. [find](https://scholar.google.com/scholar?q=Ryo+Kamoi%2C+Yusen+Zhang%2C+Sarkar+Snigdha+Sarathi+Das%2C+Ranran+Haoran+Zhang%2C+and+Rui+Zhang%2E+2025%2E+Vison%2DlyQA%3A+Large+vision+language+models+still+struggle+with+visual+perception+of+geometric+information%2E+I)
105. Bo Kang, Arun Kulshreshth, and Joseph J LaViola Jr. 2016. Analyticalink: An interactive learning environment for math word problem solving. In Proceedings of the 21st International Conference on Intelligent User Interfaces, pages 419–430. [find](https://scholar.google.com/scholar?q=Bo+Kang%2C+Arun+Kulshreshth%2C+and+Joseph+J+LaViola+Jr%2E+2016%2E+Analyticalink%3A+An+interactive+learning+environment+for+math+word+problem+solving%2E+In+Proceedings+of+the+21st+International+Conference+on+Intel)
106. Deepak Kapur. 1986. Using gröbner bases to reason about geometry problems. Journal of Symbolic Computation, 2(4):399–408. [find](https://scholar.google.com/scholar?q=Deepak+Kapur%2E+1986%2E+Using+gr%C3%B6bner+bases+to+reason+about+geometry+problems%2E+Journal+of+Symbolic+Computation%2C+2%284%29%3A399%E2%80%93408%2E)
107. Mehran Kazemi, Hamidreza Alvari, Ankit Anand, Jialin Wu, Xi Chen, and Radu Soricut. 2024a. Geomverse: A systematic evaluation of large models for geometric reasoning. In AI for Math Workshop@ ICML 2024. [find](https://scholar.google.com/scholar?q=Mehran+Kazemi%2C+Hamidreza+Alvari%2C+Ankit+Anand%2C+Jialin+Wu%2C+Xi+Chen%2C+and+Radu+Soricut%2E+2024a%2E+Geomverse%3A+A+systematic+evaluation+of+large+models+for+geometric+reasoning%2E+In+AI+for+Math+Workshop%40+ICML+202)
108. Mehran Kazemi, Nishanth Dikkala, Ankit Anand, Petar Devic, Ishita Dasgupta, Fangyu Liu, Bahare Fatemi, Pranjal Awasthi, Sreenivas Gollapudi, Dee Guo, and 1 others. 2024b. Remi: A dataset for reasoning with multiple images. Advances in Neural Information Processing Systems, 37:60088–60109. [find](https://scholar.google.com/scholar?q=Mehran+Kazemi%2C+Nishanth+Dikkala%2C+Ankit+Anand%2C+Petar+Devic%2C+Ishita+Dasgupta%2C+Fangyu+Liu%2C+Bahare+Fatemi%2C+Pranjal+Awasthi%2C+Sreenivas+Gollapudi%2C+Dee+Guo%2C+and+1+others%2E+2024b%2E+Remi%3A+A+dataset+for+reasoning)
109. Jiwoo Kim, Youngbin Kim, Ilwoong Baek, JinYeong Bak, and Jongwuk Lee. 2023. It ain’t over: A multi-aspect diverse math word problem dataset. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 14984– 15011. [find](https://scholar.google.com/scholar?q=Jiwoo+Kim%2C+Youngbin+Kim%2C+Ilwoong+Baek%2C+JinYeong+Bak%2C+and+Jongwuk+Lee%2E+2023%2E+It+ain%E2%80%99t+over%3A+A+multi%2Daspect+diverse+math+word+problem+dataset%2E+In+Proceedings+of+the+2023+Conference+on+Empirical+Methods+)
110. Kangmin Kim and Chanjun Chun. 2022. Synthetic data generator for solving korean arithmetic word problem. Mathematics, 10(19):3525. [find](https://scholar.google.com/scholar?q=Kangmin+Kim+and+Chanjun+Chun%2E+2022%2E+Synthetic+data+generator+for+solving+korean+arithmetic+word+problem%2E+Mathematics%2C+10%2819%29%3A3525%2E)
111. Thomas N Kipf and Max Welling. 2017. [Semi-supervised classification with graph convolutional networks](https://md.research.elamur.ai/free/en/papers/kipf-2016-semi-supervised-classification-graph-convolutional/). In International Conference on Learning Representations.
112. Zoltán Kovács and Jonathan H. Yu. 2021. Automated discovery of geometrical theorems in geogebra. In Proceedings 10th International Workshop on Theorem Proving Components for Educational Software, ThEdu@CADE 2021, (Remote) Carnegie Mellon University, Pittsburgh, PA, United States, 11 July 2021, volum… [find](https://scholar.google.com/scholar?q=Zolt%C3%A1n+Kov%C3%A1cs+and+Jonathan+H%2E+Yu%2E+2021%2E+Automated+discovery+of+geometrical+theorems+in+geogebra%2E+In+Proceedings+10th+International+Workshop+on+Theorem+Proving+Components+for+Educational+Software%2C+ThEd)
113. Ryan Krueger, Jesse Michael Han, and Daniel Selsam. 2021. Automatically building diagrams for olympiad geometry problems. In CADE, pages 577–588. [find](https://scholar.google.com/scholar?q=Ryan+Krueger%2C+Jesse+Michael+Han%2C+and+Daniel+Selsam%2E+2021%2E+Automatically+building+diagrams+for+olympiad+geometry+problems%2E+In+CADE%2C+pages+577%E2%80%93588%2E)
114. Sergey Kurbatov, Igor Fominykh, and Aleksandr Vorobyev. 2021. Cognitive patterns for semantic presentation of natural-language descriptions of well-formalizable problems. In Russian Conference on Artificial Intelligence, pages 317–330. Springer. [find](https://scholar.google.com/scholar?q=Sergey+Kurbatov%2C+Igor+Fominykh%2C+and+Aleksandr+Vorobyev%2E+2021%2E+Cognitive+patterns+for+semantic+presentation+of+natural%2Dlanguage+descriptions+of+well%2Dformalizable+problems%2E+In+Russian+Conference+on+Arti)
115. Sergey S Kurbatov and Igor B Fominykh. 2022. Complex modeling of inductive and deductive reasoning by the example of a planimetric problem solver. In International Conference on Intelligent Information Technologies for Industry, pages 454–462. Springer. [find](https://scholar.google.com/scholar?q=Sergey+S+Kurbatov+and+Igor+B+Fominykh%2E+2022%2E+Complex+modeling+of+inductive+and+deductive+reasoning+by+the+example+of+a+planimetric+problem+solver%2E+In+International+Conference+on+Intelligent+Informatio)
116. Sergey S Kurbatov, Igor B Fominykh, and Aleksandr B Vorobyev. 2020. Ontology-controlled geometric solver. In Artificial Intelligence: 18th Russian Conference, RCAI 2020, Moscow, Russia, October 10–16, 2020, Proceedings 18, pages 262–273. Springer. [find](https://scholar.google.com/scholar?q=Sergey+S+Kurbatov%2C+Igor+B+Fominykh%2C+and+Aleksandr+B+Vorobyev%2E+2020%2E+Ontology%2Dcontrolled+geometric+solver%2E+In+Artificial+Intelligence%3A+18th+Russian+Conference%2C+RCAI+2020%2C+Moscow%2C+Russia%2C+October+10%E2%80%9316%2C)
117. Sergey S Kurbatov, Xenia A Naidenova, Vyacheslav P Ganapolsky, and Tatyana A Martirova. 2022. Natural language processing and functioning ontological solver with visualization in an integrated system. In Proceedings of SAI Intelligent Systems Conference, pages 669–682. Springer. [find](https://scholar.google.com/scholar?q=Sergey+S+Kurbatov%2C+Xenia+A+Naidenova%2C+Vyacheslav+P+Ganapolsky%2C+and+Tatyana+A+Martirova%2E+2022%2E+Natural+language+processing+and+functioning+ontological+solver+with+visualization+in+an+integrated+system%2E)
118. Sergeyi S Kurbatov. 2021. Linguistic processor integration for solving planimetric problems. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 15(4):1–14. [find](https://scholar.google.com/scholar?q=Sergeyi+S+Kurbatov%2E+2021%2E+Linguistic+processor+integration+for+solving+planimetric+problems%2E+International+Journal+of+Cognitive+Informatics+and+Natural+Intelligence+%28IJCINI%29%2C+15%284%29%3A1%E2%80%9314%2E)
119. Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324. [find](https://scholar.google.com/scholar?q=Yann+LeCun%2C+L%C3%A9on+Bottou%2C+Yoshua+Bengio%2C+and+Patrick+Haffner%2E+1998%2E+Gradient%2Dbased+learning+applied+to+document+recognition%2E+Proceedings+of+the+IEEE%2C+86%2811%29%3A2278%E2%80%932324%2E)
120. Jeongwoo Lee, Kwangsuk Park, and Jihyeon Park. 2025a. Vista: Visual integrated system for tailored automation in math problem generation using llm. In Large Foundation Models for Educational Assessment, pages 136–156. PMLR. [find](https://scholar.google.com/scholar?q=Jeongwoo+Lee%2C+Kwangsuk+Park%2C+and+Jihyeon+Park%2E+2025a%2E+Vista%3A+Visual+integrated+system+for+tailored+automation+in+math+problem+generation+using+llm%2E+In+Large+Foundation+Models+for+Educational+Assessmen)
121. Jimin Lee, Steven-Shine Chen, and Paul Pu Liang. 2025b. Interactive sketchpad: A multimodal tutoring system for collaborative, visual problem-solving. In Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, pages 1–14. [find](https://scholar.google.com/scholar?q=Jimin+Lee%2C+Steven%2DShine+Chen%2C+and+Paul+Pu+Liang%2E+2025b%2E+Interactive+sketchpad%3A+A+multimodal+tutoring+system+for+collaborative%2C+visual+problem%2Dsolving%2E+In+Proceedings+of+the+Extended+Abstracts+of+the+C)
122. Bin Lei, Yi Zhang, Shan Zuo, Ali Payani, and Caiwen Ding. 2024. Macm: Utilizing a multi-agent system for condition mining in solving complex mathematical problems. In The Thirty-eighth Annual Conference on Neural Information Processing Systems. [find](https://scholar.google.com/scholar?q=Bin+Lei%2C+Yi+Zhang%2C+Shan+Zuo%2C+Ali+Payani%2C+and+Caiwen+Ding%2E+2024%2E+Macm%3A+Utilizing+a+multi%2Dagent+system+for+condition+mining+in+solving+complex+mathematical+problems%2E+In+The+Thirty%2Deighth+Annual+Conferen)
123. Dawei Li, Bohan Jiang, Liangjie Huang, Alimohammad Beigi, Chengshuai Zhao, Zhen Tan, Amrita Bhattacharjee, Yuxuan Jiang, Canyu Chen, Tianhao Wu, and 1 others. 2025a. From generation to judgment: Opportunities and challenges of llm-as-a-judge. In Proceedings of the 2025 Conference on Empirical Method… [find](https://scholar.google.com/scholar?q=Dawei+Li%2C+Bohan+Jiang%2C+Liangjie+Huang%2C+Alimohammad+Beigi%2C+Chengshuai+Zhao%2C+Zhen+Tan%2C+Amrita+Bhattacharjee%2C+Yuxuan+Jiang%2C+Canyu+Chen%2C+Tianhao+Wu%2C+and+1+others%2E+2025a%2E+From+generation+to+judgment%3A+Oppor)
124. Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. 2022. [Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation](https://md.research.elamur.ai/free/en/papers/li-2022-blip-bootstrapping-language-image-pre/). In International conference on machine learning, pages 12888–12900. PMLR.
125. Lei Li, Yuqi Wang, Runxin Xu, Peiyi Wang, Xiachong Feng, Lingpeng Kong, and Qi Liu. 2024a. [Multimodal arxiv: A dataset for improving scientific comprehension of large vision-language models](https://md.research.elamur.ai/free/en/papers/li-2024-multimodal-arxiv-dataset-improving-scientific/). In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Pa…
126. Lei Li, Zongkai Yang, Mao Chen, Xicheng Peng, Jianwen Sun, Zhonghua Yan, and Sannyuya Liu. 2024b. Automated generation of geometry proof problems based on point geometry identity. Journal of Automated Reasoning, 68(2):11. [find](https://scholar.google.com/scholar?q=Lei+Li%2C+Zongkai+Yang%2C+Mao+Chen%2C+Xicheng+Peng%2C+Jianwen+Sun%2C+Zhonghua+Yan%2C+and+Sannyuya+Liu%2E+2024b%2E+Automated+generation+of+geometry+proof+problems+based+on+point+geometry+identity%2E+Journal+of+Automated)
127. Nianqi Li, Zujie Liang, Siyu Yuan, Jiaqing Liang, Feng Wei, and Yanghua Xiao. 2024c. Multilingpot: Enhancing mathematical reasoning with multilingual program fine-tuning. arXiv preprint arXiv:2412.12609. — [arXiv:2412.12609](https://arxiv.org/abs/2412.12609) [find](https://scholar.google.com/scholar?q=Nianqi+Li%2C+Zujie+Liang%2C+Siyu+Yuan%2C+Jiaqing+Liang%2C+Feng+Wei%2C+and+Yanghua+Xiao%2E+2024c%2E+Multilingpot%3A+Enhancing+mathematical+reasoning+with+multilingual+program+fine%2Dtuning%2E+arXiv+preprint+arXiv%3A2412%2E126)
128. Zhaoyu Li, Jialiang Sun, Logan Murphy, Qidong Su, Zenan Li, Xian Zhang, Kaiyu Yang, and Xujie Si. 2024d. A survey on deep learning for theorem proving. In First Conference on Language Modeling. [find](https://scholar.google.com/scholar?q=Zhaoyu+Li%2C+Jialiang+Sun%2C+Logan+Murphy%2C+Qidong+Su%2C+Zenan+Li%2C+Xian+Zhang%2C+Kaiyu+Yang%2C+and+Xujie+Si%2E+2024d%2E+A+survey+on+deep+learning+for+theorem+proving%2E+In+First+Conference+on+Language+Modeling%2E)
129. Zhaoyu Li, Jialiang Sun, Logan Murphy, Qidong Su, Zenan Li, Xian Zhang, Kaiyu Yang, and Xujie Si. 2024e. A survey on deep learning for theorem proving. In First Conference on Language Modeling. [find](https://scholar.google.com/scholar?q=Zhaoyu+Li%2C+Jialiang+Sun%2C+Logan+Murphy%2C+Qidong+Su%2C+Zenan+Li%2C+Xian+Zhang%2C+Kaiyu+Yang%2C+and+Xujie+Si%2E+2024e%2E+A+survey+on+deep+learning+for+theorem+proving%2E+In+First+Conference+on+Language+Modeling%2E)
130. Zhihao Li, Yao Du, Yang Liu, Yan Zhang, Yufang Liu, Mengdi Zhang, and Xunliang Cai. 2024f. Eagle: Elevating geometric reasoning through llm-empowered visual instruction tuning. arXiv preprint arXiv:2408.11397. — [arXiv:2408.11397](https://arxiv.org/abs/2408.11397) [find](https://scholar.google.com/scholar?q=Zhihao+Li%2C+Yao+Du%2C+Yang+Liu%2C+Yan+Zhang%2C+Yufang+Liu%2C+Mengdi+Zhang%2C+and+Xunliang+Cai%2E+2024f%2E+Eagle%3A+Elevating+geometric+reasoning+through+llm%2Dempowered+visual+instruction+tuning%2E+arXiv+preprint+arXiv%3A24)
131. Zhong-Zhi Li, Ming-Liang Zhang, Fei Yin, and Cheng-Lin Liu. 2024g. Lans: A layout-aware neural solver for plane geometry problem. In Findings of the Association for Computational Linguistics ACL 2024, pages 2596–2608. [find](https://scholar.google.com/scholar?q=Zhong%2DZhi+Li%2C+Ming%2DLiang+Zhang%2C+Fei+Yin%2C+and+Cheng%2DLin+Liu%2E+2024g%2E+Lans%3A+A+layout%2Daware+neural+solver+for+plane+geometry+problem%2E+In+Findings+of+the+Association+for+Computational+Linguistics+ACL+2024%2C)
132. Zhongzhi Li, Ming-Liang Zhang, Pei-Jie Wang, Jian Xu, Rui-Song Zhang, Yin Fei, Zhi-Long Ji, Jin-Feng Bai, Zhen-Ru Pan, Jiaxin Zhang, and 1 others. 2025b. Cmmath: A chinese multi-modal math skill evaluation benchmark for foundation models. In Proceedings of the 31st International Conference on Comput… [find](https://scholar.google.com/scholar?q=Zhongzhi+Li%2C+Ming%2DLiang+Zhang%2C+Pei%2DJie+Wang%2C+Jian+Xu%2C+Rui%2DSong+Zhang%2C+Yin+Fei%2C+Zhi%2DLong+Ji%2C+Jin%2DFeng+Bai%2C+Zhen%2DRu+Pan%2C+Jiaxin+Zhang%2C+and+1+others%2E+2025b%2E+Cmmath%3A+A+chinese+multi%2Dmodal+math+skill+evalu)
133. Zhenwen Liang, Tianyu Yang, Jipeng Zhang, and Xiangliang Zhang. 2023. Unimath: A foundational and multimodal mathematical reasoner. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 7126–7133. [find](https://scholar.google.com/scholar?q=Zhenwen+Liang%2C+Tianyu+Yang%2C+Jipeng+Zhang%2C+and+Xiangliang+Zhang%2E+2023%2E+Unimath%3A+A+foundational+and+multimodal+mathematical+reasoner%2E+In+Proceedings+of+the+2023+Conference+on+Empirical+Methods+in+Natura)
134. Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár. 2017. [Focal loss for dense object detection](https://md.research.elamur.ai/free/en/papers/lin-2017-focal-loss-dense-object-detection/). In Proceedings of the IEEE international conference on computer vision, pages 2980–2988.
135. Zi-Hao Lin, Shun-Xin Xiao, Zi-Rong Chen, Jian-Min Li, Da-Han Wang, and Xu-Yao Zhang. 2024. Sans: Spatial-aware neural solver for plane geometry problem. In International Conference on Pattern Recognition, pages 183–196. Springer. [find](https://scholar.google.com/scholar?q=Zi%2DHao+Lin%2C+Shun%2DXin+Xiao%2C+Zi%2DRong+Chen%2C+Jian%2DMin+Li%2C+Da%2DHan+Wang%2C+and+Xu%2DYao+Zhang%2E+2024%2E+Sans%3A+Spatial%2Daware+neural+solver+for+plane+geometry+problem%2E+In+International+Conference+on+Pattern+Recognit)
136. Haotian Liu, Chunyuan Li, Yuheng Li, and Yong Jae Lee. 2024a. [Improved baselines with visual instruction tuning](https://md.research.elamur.ai/free/en/papers/liu-2023-improved-baselines-visual-instruction-tuning/). In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 26296–26306.
137. Hongwei Liu, Zilong Zheng, Yuxuan Qiao, Haodong Duan, Zhiwei Fei, Fengzhe Zhou, Wenwei Zhang, Songyang Zhang, Dahua Lin, and Kai Chen. 2024b. Mathbench: Evaluating the theory and application proficiency of llms with a hierarchical mathematics benchmark. In Findings of the Association for Computation… [find](https://scholar.google.com/scholar?q=Hongwei+Liu%2C+Zilong+Zheng%2C+Yuxuan+Qiao%2C+Haodong+Duan%2C+Zhiwei+Fei%2C+Fengzhe+Zhou%2C+Wenwei+Zhang%2C+Songyang+Zhang%2C+Dahua+Lin%2C+and+Kai+Chen%2E+2024b%2E+Mathbench%3A+Evaluating+the+theory+and+application+proficien)
138. Lu Liu, Xiaoqing Lu, Songping Fu, Jingwei Qu, Liangcai Gao, and Zhi Tang. 2014a. Plane geometry figure retrieval based on bilayer geometric attributed graph matching. In 2014 22nd International Conference on Pattern Recognition, pages 309–314. IEEE. [find](https://scholar.google.com/scholar?q=Lu+Liu%2C+Xiaoqing+Lu%2C+Songping+Fu%2C+Jingwei+Qu%2C+Liangcai+Gao%2C+and+Zhi+Tang%2E+2014a%2E+Plane+geometry+figure+retrieval+based+on+bilayer+geometric+attributed+graph+matching%2E+In+2014+22nd+International+Confer)
139. Lu Liu, Xiaoqing Lu, Keqiang Li, Jingwei Qu, Liangcai Gao, and Zhi Tang. 2014b. Plane geometry figure retrieval with bag of shapes. In 2014 11th IAPR International Workshop on Document Analysis Systems, pages 1–5. IEEE. [find](https://scholar.google.com/scholar?q=Lu+Liu%2C+Xiaoqing+Lu%2C+Keqiang+Li%2C+Jingwei+Qu%2C+Liangcai+Gao%2C+and+Zhi+Tang%2E+2014b%2E+Plane+geometry+figure+retrieval+with+bag+of+shapes%2E+In+2014+11th+IAPR+International+Workshop+on+Document+Analysis+System)
140. Lu Liu, Xiaoqing Lu, Yuan Liao, Yongtao Wang, and Zhi Tang. 2016. Improving retrieval of plane geometry figure with learning to rank. Pattern Recognition Letters, 83:423–429. [find](https://scholar.google.com/scholar?q=Lu+Liu%2C+Xiaoqing+Lu%2C+Yuan+Liao%2C+Yongtao+Wang%2C+and+Zhi+Tang%2E+2016%2E+Improving+retrieval+of+plane+geometry+figure+with+learning+to+rank%2E+Pattern+Recognition+Letters%2C+83%3A423%E2%80%93429%2E)
141. Qing Tang Liu, Huan Huang, and Lin Jing Wu. 2012. Using restricted natural language for geometric construction. Applied Mechanics and Materials, 145:465–469. [find](https://scholar.google.com/scholar?q=Qing+Tang+Liu%2C+Huan+Huang%2C+and+Lin+Jing+Wu%2E+2012%2E+Using+restricted+natural+language+for+geometric+construction%2E+Applied+Mechanics+and+Materials%2C+145%3A465%E2%80%93469%2E)
142. Tengxiao Liu, Qipeng Guo, Yuqing Yang, Xiangkun Hu, Yue Zhang, Xipeng Qiu, and Zheng Zhang. 2023. Plan, verify and switch: Integrated reasoning with diverse x-of-thoughts. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 2807–2822. [find](https://scholar.google.com/scholar?q=Tengxiao+Liu%2C+Qipeng+Guo%2C+Yuqing+Yang%2C+Xiangkun+Hu%2C+Yue+Zhang%2C+Xipeng+Qiu%2C+and+Zheng+Zhang%2E+2023%2E+Plan%2C+verify+and+switch%3A+Integrated+reasoning+with+diverse+x%2Dof%2Dthoughts%2E+In+Proceedings+of+the+2023+C)
143. Wentao Liu, Qianjun Pan, Yi Zhang, Zhuo Liu, Ji Wu, Jie Zhou, Aimin Zhou, Qin Chen, Bo Jiang, and Liang He. 2025a. [Cmm-math: A chinese multimodal math dataset to evaluate and enhance the mathematics reasoning of large multimodal models](https://md.research.elamur.ai/free/en/papers/liu-2024-cmm-math-chinese-multimodal-math/). In Proceedings of the 33rd ACM International Conference on Mult…
144. Xiangyan Liu, Jinjie Ni, Zijian Wu, Chao Du, Longxu Dou, Haonan Wang, Tianyu Pang, and Michael Qizhe Shieh. 2025b. Noisyrollout: Reinforcing visual reasoning with data augmentation. In The Thirty-ninth Annual Conference on Neural Information Processing Systems. [find](https://scholar.google.com/scholar?q=Xiangyan+Liu%2C+Jinjie+Ni%2C+Zijian+Wu%2C+Chao+Du%2C+Longxu+Dou%2C+Haonan+Wang%2C+Tianyu+Pang%2C+and+Michael+Qizhe+Shieh%2E+2025b%2E+Noisyrollout%3A+Reinforcing+visual+reasoning+with+data+augmentation%2E+In+The+Thirty%2Dnint)
145. Yexiang Liu, Jie Cao, Zekun Li, Ran He, and Tieniu Tan. 2025c. Breaking mental set to improve reasoning through diverse multi-agent debate. In The Thirteenth International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Yexiang+Liu%2C+Jie+Cao%2C+Zekun+Li%2C+Ran+He%2C+and+Tieniu+Tan%2E+2025c%2E+Breaking+mental+set+to+improve+reasoning+through+diverse+multi%2Dagent+debate%2E+In+The+Thirteenth+International+Conference+on+Learning+Repre)
146. Yifan Liu, Keyu Ding, and Yi Zhou. 2019a. Aifu at semeval-2019 task 10: A symbolic and sub-symbolic integrated system for sat math question answering. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 900–906. [find](https://scholar.google.com/scholar?q=Yifan+Liu%2C+Keyu+Ding%2C+and+Yi+Zhou%2E+2019a%2E+Aifu+at+semeval%2D2019+task+10%3A+A+symbolic+and+sub%2Dsymbolic+integrated+system+for+sat+math+question+answering%2E+In+Proceedings+of+the+13th+International+Workshop)
147. Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019b. [Roberta: A robustly optimized bert pretraining approach](https://md.research.elamur.ai/free/en/papers/liu-2019-roberta-robustly-optimized-bert-pretraining/). arXiv preprint arXiv:1907.11692.
148. Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. 2021. [Swin transformer: Hierarchical vision transformer using shifted windows](https://md.research.elamur.ai/free/en/papers/liu-2021-swin-transformer-hierarchical-vision-transformer/). In Proceedings of the IEEE/CVF international conference on computer vision, pages 10012–10022.
149. Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie. 2022. A convnet for the 2020s. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11976–11986. [find](https://scholar.google.com/scholar?q=Zhuang+Liu%2C+Hanzi+Mao%2C+Chao%2DYuan+Wu%2C+Christoph+Feichtenhofer%2C+Trevor+Darrell%2C+and+Saining+Xie%2E+2022%2E+A+convnet+for+the+2020s%2E+In+Proceedings+of+the+IEEE%2FCVF+conference+on+computer+vision+and+pattern+r)
150. Pan Lu, Hritik Bansal, Tony Xia, Jiacheng Liu, Chunyuan Li, Hannaneh Hajishirzi, Hao Cheng, Kai-Wei Chang, Michel Galley, and Jianfeng Gao. 2024. [Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts](https://md.research.elamur.ai/free/en/papers/lu-2023-mathvista-evaluating-mathematical-reasoning-foundation/). In The Twelfth International Conference on Learning Representations.
151. Pan Lu, Ran Gong, Shibiao Jiang, Liang Qiu, Siyuan Huang, Xiaodan Liang, and Song-Chun Zhu. 2021. [Inter-gps: Interpretable geometry problem solving with formal language and symbolic reasoning](https://md.research.elamur.ai/free/en/papers/lu-2021-inter-gps-interpretable-geometry-problem/). In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th Int…
152. Pan Lu, Liang Qiu, Wenhao Yu, Sean Welleck, and Kai-Wei Chang. 2023. A survey of deep learning for mathematical reasoning. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 14605– 14631. [find](https://scholar.google.com/scholar?q=Pan+Lu%2C+Liang+Qiu%2C+Wenhao+Yu%2C+Sean+Welleck%2C+and+Kai%2DWei+Chang%2E+2023%2E+A+survey+of+deep+learning+for+mathematical+reasoning%2E+In+Proceedings+of+the+61st+Annual+Meeting+of+the+Association+for+Computationa)
153. Xiaoqing Lu, Lu Liu, Zhi Tang, and Haibin Ling. 2015. Overlapped-triangle analysis with hierarchical ranking of dominance. In 2015 13th International Conference on Document Analysis and Recognition (IC-DAR), pages 791–795. IEEE. [find](https://scholar.google.com/scholar?q=Xiaoqing+Lu%2C+Lu+Liu%2C+Zhi+Tang%2C+and+Haibin+Ling%2E+2015%2E+Overlapped%2Dtriangle+analysis+with+hierarchical+ranking+of+dominance%2E+In+2015+13th+International+Conference+on+Document+Analysis+and+Recognition+%28I)
154. Ruilin Luo, Zhuofan Zheng, Lei Wang, Yifan Wang, Xinzhe Ni, Zicheng Lin, Songtao Jiang, Yiyao Yu, Chufan Shi, Ruihang Chu, and 1 others. 2025. Unlocking multimodal mathematical reasoning via process reward model. In The Thirty-ninth Annual Conference on Neural Information Processing Systems. [find](https://scholar.google.com/scholar?q=Ruilin+Luo%2C+Zhuofan+Zheng%2C+Lei+Wang%2C+Yifan+Wang%2C+Xinzhe+Ni%2C+Zicheng+Lin%2C+Songtao+Jiang%2C+Yiyao+Yu%2C+Chufan+Shi%2C+Ruihang+Chu%2C+and+1+others%2E+2025%2E+Unlocking+multimodal+mathematical+reasoning+via+process+r)
155. Bin Ma, Pengpeng Jian, Cong Pan, Yanli Wang, and Wei Ma. 2024. A geometric neural solving method based on a diagram text information fusion analysis. Scientific Reports, 14(1):31906. [find](https://scholar.google.com/scholar?q=Bin+Ma%2C+Pengpeng+Jian%2C+Cong+Pan%2C+Yanli+Wang%2C+and+Wei+Ma%2E+2024%2E+A+geometric+neural+solving+method+based+on+a+diagram+text+information+fusion+analysis%2E+Scientific+Reports%2C+14%281%29%3A31906%2E)
156. Jingkun Ma, Runzhe Zhan, Yang Li, Di Sun, Hou Pong Chan, Lidia S Chao, and Derek F Wong. 2025. [Visaidmath: Benchmarking visual-aided mathematical reasoning](https://md.research.elamur.ai/free/en/papers/ma-2026-visaidmath-benchmarking-visual-aided-mathematical/). In 1st Workshop on VLM4RWD@ NeurIPS 2025.
157. Jaroslav Macke, Jiri Sedlar, Miroslav Olsak, Josef Urban, and Josef Sivic. 2021. Learning to solve geometric construction problems from images. In International Conference on Intelligent Computer Mathematics, pages 167–184. Springer. [find](https://scholar.google.com/scholar?q=Jaroslav+Macke%2C+Jiri+Sedlar%2C+Miroslav+Olsak%2C+Josef+Urban%2C+and+Josef+Sivic%2E+2021%2E+Learning+to+solve+geometric+construction+problems+from+images%2E+In+International+Conference+on+Intelligent+Computer+Math)
158. Abeer Mahgoub, Ghada Khoriba, and ElHassan Anas ElSabry. 2024. Mathematical problem solving in arabic: Assessing large language models. Procedia Computer Science, 244:86–95. [find](https://scholar.google.com/scholar?q=Abeer+Mahgoub%2C+Ghada+Khoriba%2C+and+ElHassan+Anas+ElSabry%2E+2024%2E+Mathematical+problem+solving+in+arabic%3A+Assessing+large+language+models%2E+Procedia+Computer+Science%2C+244%3A86%E2%80%9395%2E)
159. Takuya Matsuzaki, Takumi Ito, Hidenao Iwane, Hirokazu Anai, and Noriko H Arai. 2017. Semantic parsing of pre-university math problems. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2131–2141. [find](https://scholar.google.com/scholar?q=Takuya+Matsuzaki%2C+Takumi+Ito%2C+Hidenao+Iwane%2C+Hirokazu+Anai%2C+and+Noriko+H+Arai%2E+2017%2E+Semantic+parsing+of+pre%2Duniversity+math+problems%2E+In+Proceedings+of+the+55th+Annual+Meeting+of+the+Association+for+)
160. Maxwell-Jia. 2024. Aime 2024. [https://huggingface.co/datasets/Maxwell-Jia/](https://huggingface.co/datasets/Maxwell-Jia/) AIME_2024. Accessed: 2025-06-06. [↗](https://huggingface.co/datasets/Maxwell-Jia/)
161. Chamupathi Mendis, Dhanushka Lahiru, Naduni Pa-mudika, Supun Madushanka, Surangika Ranathunga, and Gihan Dias. 2017. Automatic assessment of student answers for geometric theorem proving questions. In 2017 Moratuwa Engineering Research Conference (MERCon), pages 413–418. IEEE. [find](https://scholar.google.com/scholar?q=Chamupathi+Mendis%2C+Dhanushka+Lahiru%2C+Naduni+Pa%2Dmudika%2C+Supun+Madushanka%2C+Surangika+Ranathunga%2C+and+Gihan+Dias%2E+2017%2E+Automatic+assessment+of+student+answers+for+geometric+theorem+proving+questions%2E+In)
162. Swaroop Mishra, Matthew Finlayson, Pan Lu, Leonard Tang, Sean Welleck, Chitta Baral, Tanmay Rajpurohit, Oyvind Tafjord, Ashish Sabharwal, Peter Clark, and 1 others. 2022a. [Lila: A unified benchmark for mathematical reasoning](https://md.research.elamur.ai/free/en/papers/mishra-2022-lila-unified-benchmark-mathematical-reasoning/). In Proceedings of the 2022 Conference on Empirical Methods in Natural Lang…
163. Swaroop Mishra, Arindam Mitra, Neeraj Varshney, Bhavdeep Sachdeva, Peter Clark, Chitta Baral, and Ashwin Kalyan. 2022b. Numglue: A suite of fundamental yet challenging mathematical reasoning tasks. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1:… [find](https://scholar.google.com/scholar?q=Swaroop+Mishra%2C+Arindam+Mitra%2C+Neeraj+Varshney%2C+Bhavdeep+Sachdeva%2C+Peter+Clark%2C+Chitta+Baral%2C+and+Ashwin+Kalyan%2E+2022b%2E+Numglue%3A+A+suite+of+fundamental+yet+challenging+mathematical+reasoning+tasks%2E+In)
164. Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller. 2013. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602. [find](https://scholar.google.com/scholar?q=Volodymyr+Mnih%2C+Koray+Kavukcuoglu%2C+David+Silver%2C+Alex+Graves%2C+Ioannis+Antonoglou%2C+Daan+Wierstra%2C+and+Martin+Riedmiller%2E+2013%2E+Playing+atari+with+deep+reinforcement+learning%2E+arXiv+preprint+arXiv%3A1312%2E)
165. Spyridon Mouselinos, Henryk Michalewski, and Mateusz Malinowski. 2024. Beyond lines and circles: Unveiling the geometric reasoning gap in large language models. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 6192–6222. [find](https://scholar.google.com/scholar?q=Spyridon+Mouselinos%2C+Henryk+Michalewski%2C+and+Mateusz+Malinowski%2E+2024%2E+Beyond+lines+and+circles%3A+Unveiling+the+geometric+reasoning+gap+in+large+language+models%2E+In+Findings+of+the+Association+for+Comp)
166. Logan Murphy, Kaiyu Yang, Jialiang Sun, Zhaoyu Li, Anima Anandkumar, and Xujie Si. 2024. Autoformalizing euclidean geometry. In International Conference on Machine Learning, pages 36847–36893. PMLR. [find](https://scholar.google.com/scholar?q=Logan+Murphy%2C+Kaiyu+Yang%2C+Jialiang+Sun%2C+Zhaoyu+Li%2C+Anima+Anandkumar%2C+and+Xujie+Si%2E+2024%2E+Autoformalizing+euclidean+geometry%2E+In+International+Conference+on+Machine+Learning%2C+pages+36847%E2%80%9336893%2E+PMLR%2E)
167. Julien Narboux, Predrag Janicic, and Jacques Fleuriot. 2018. Computer-assisted theorem proving in synthetic geometry. Handbook of Geometric Constraint Systems Principles, pages 25–73. [find](https://scholar.google.com/scholar?q=Julien+Narboux%2C+Predrag+Janicic%2C+and+Jacques+Fleuriot%2E+2018%2E+Computer%2Dassisted+theorem+proving+in+synthetic+geometry%2E+Handbook+of+Geometric+Constraint+Systems+Principles%2C+pages+25%E2%80%9373%2E)
168. Maizhen Ning, Qiu-Feng Wang, Kaizhu Huang, and Xiaowei Huang. 2023. A symbolic characters aware model for solving geometry problems. In Proceedings of the 31st ACM International Conference on Multimedia, pages 7767–7775. [find](https://scholar.google.com/scholar?q=Maizhen+Ning%2C+Qiu%2DFeng+Wang%2C+Kaizhu+Huang%2C+and+Xiaowei+Huang%2E+2023%2E+A+symbolic+characters+aware+model+for+solving+geometry+problems%2E+In+Proceedings+of+the+31st+ACM+International+Conference+on+Multimed)
169. Maizhen Ning, Zihao Zhou, Qiufeng Wang, Xiaowei Huang, and Kaizhu Huang. 2025. Gns: Solving plane geometry problems by neural-symbolic reasoning with multi-modal llms. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 24957–24965. [find](https://scholar.google.com/scholar?q=Maizhen+Ning%2C+Zihao+Zhou%2C+Qiufeng+Wang%2C+Xiaowei+Huang%2C+and+Kaizhu+Huang%2E+2025%2E+Gns%3A+Solving+plane+geometry+problems+by+neural%2Dsymbolic+reasoning+with+multi%2Dmodal+llms%2E+In+Proceedings+of+the+AAAI+Confe)
170. Kazuaki Okada, Masashi Kudo, and Hayato Yamana. 2023. Estimating answer strategies using online handwritten data: A study using geometry problems. In Proceedings of the 15th International Conference on Education Technology and Computers, pages 308– 314. [find](https://scholar.google.com/scholar?q=Kazuaki+Okada%2C+Masashi+Kudo%2C+and+Hayato+Yamana%2E+2023%2E+Estimating+answer+strategies+using+online+handwritten+data%3A+A+study+using+geometry+problems%2E+In+Proceedings+of+the+15th+International+Conference+o)
171. Chibuike Onuoha, Yusuke Haga, Dilshad Ferdousi, and Truong Cong Thang. 2025. Multimodal large language models for high school mathematical reasoning: Impact of input modality and artifacts. In 2025 IEEE International Conference on Artificial Intelligence and Mechatronics Systems (AIMS), pages 1–6. I… [find](https://scholar.google.com/scholar?q=Chibuike+Onuoha%2C+Yusuke+Haga%2C+Dilshad+Ferdousi%2C+and+Truong+Cong+Thang%2E+2025%2E+Multimodal+large+language+models+for+high+school+mathematical+reasoning%3A+Impact+of+input+modality+and+artifacts%2E+In+2025+IE)
172. Cong Pan, Pengpeng Jian, Bin Ma, Ling Feng, and Xuemei Liu. 2023. The geometric neural solution combined with text diagram parsing. In 2023 International Conference on Intelligent Education and Intelligent Research (IEIR), pages 1–7. IEEE. [find](https://scholar.google.com/scholar?q=Cong+Pan%2C+Pengpeng+Jian%2C+Bin+Ma%2C+Ling+Feng%2C+and+Xuemei+Liu%2E+2023%2E+The+geometric+neural+solution+combined+with+text+diagram+parsing%2E+In+2023+International+Conference+on+Intelligent+Education+and+Intell)
173. Yicheng Pan, Zhenrong Zhang, Pengfei Hu, Jiefeng Ma, Jun Du, Jianshu Zhang, Quan Liu, Jianqing Gao, and Feng Ma. 2025. Enhancing the geometric problem-solving ability of multimodal llms via symbolic-neural integration. In Proceedings of the 33rd ACM International Conference on Multimedia, pages 5394… [find](https://scholar.google.com/scholar?q=Yicheng+Pan%2C+Zhenrong+Zhang%2C+Pengfei+Hu%2C+Jiefeng+Ma%2C+Jun+Du%2C+Jianshu+Zhang%2C+Quan+Liu%2C+Jianqing+Gao%2C+and+Feng+Ma%2E+2025%2E+Enhancing+the+geometric+problem%2Dsolving+ability+of+multimodal+llms+via+symbolic%2Dn)
174. Yicheng Pan, Zhenrong Zhang, Jiefeng Ma, Pengfei Hu, Jun Du, Qing Wang, Jianshu Zhang, Dan Liu, and Si Wei. 2024. Maths: Multimodal transformer-based human-readable solver. In 2024 IEEE International Conference on Multimedia and Expo (ICME), pages 1–6. IEEE. [find](https://scholar.google.com/scholar?q=Yicheng+Pan%2C+Zhenrong+Zhang%2C+Jiefeng+Ma%2C+Pengfei+Hu%2C+Jun+Du%2C+Qing+Wang%2C+Jianshu+Zhang%2C+Dan+Liu%2C+and+Si+Wei%2E+2024%2E+Maths%3A+Multimodal+transformer%2Dbased+human%2Dreadable+solver%2E+In+2024+IEEE+International+)
175. Jieun Park, Sungeun Park, Hyungbae Jeon, and Joon-Ho Lim. 2024. What is the true performance of large multimodal models in visual context-based mathematical reasoning? an analysis of multiple datasets and future research directions. In 2024 15th International Conference on Information and Communicat… [find](https://scholar.google.com/scholar?q=Jieun+Park%2C+Sungeun+Park%2C+Hyungbae+Jeon%2C+and+Joon%2DHo+Lim%2E+2024%2E+What+is+the+true+performance+of+large+multimodal+models+in+visual+context%2Dbased+mathematical+reasoning%3F+an+analysis+of+multiple+datasets)
176. Gollam Rabby, Farhana Keya, Parvez Zamil, and Sören Auer. 2024. Mc-nest–enhancing mathematical reasoning in large language models with a monte carlo nash equilibrium self-refine tree. arXiv preprint arXiv:2411.15645. [find](https://scholar.google.com/scholar?q=Gollam+Rabby%2C+Farhana+Keya%2C+Parvez+Zamil%2C+and+S%C3%B6ren+Auer%2E+2024%2E+Mc%2Dnest%E2%80%93enhancing+mathematical+reasoning+in+large+language+models+with+a+monte+carlo+nash+equilibrium+self%2Drefine+tree%2E+arXiv+preprint+a)
177. Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, and 1 others. 2021. [Learning transferable visual models from natural language supervision](https://md.research.elamur.ai/free/en/papers/radford-2021-learning-transferable-visual-models-natural/). In International conference on machine learning, pages 8748–8…
178. Shuai Peng, Di Fu, Liangcai Gao, Xiuqin Zhong, Hongguang Fu, and Zhi Tang. 2024. Multimath: Bridging visual and mathematical reasoning for large language models. arXiv preprint arXiv:2409.00147. — [arXiv:2409.00147](https://arxiv.org/abs/2409.00147) [find](https://scholar.google.com/scholar?q=Shuai+Peng%2C+Di+Fu%2C+Liangcai+Gao%2C+Xiuqin+Zhong%2C+Hongguang+Fu%2C+and+Zhi+Tang%2E+2024%2E+Multimath%3A+Bridging+visual+and+mathematical+reasoning+for+large+language+models%2E+arXiv+preprint+arXiv%3A2409%2E00147%2E+%E2%80%94+%5Bar)
179. Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. 2023. [Direct preference optimization: Your language model is secretly a reward model](https://md.research.elamur.ai/free/en/papers/rafailov-2023-direct-preference-optimization-language-model/). Advances in Neural Information Processing Systems, 36:53728– 53741.
180. Shuai Peng, Di Fu, Yijun Liang, Liangcai Gao, and Zhi Tang. 2023. Geodrl: A self-learning framework for geometry problem solving using reinforcement learning in deductive reasoning. In Findings of the Association for Computational Linguistics: ACL 2023, pages 13468–13480. [find](https://scholar.google.com/scholar?q=Shuai+Peng%2C+Di+Fu%2C+Yijun+Liang%2C+Liangcai+Gao%2C+and+Zhi+Tang%2E+2023%2E+Geodrl%3A+A+self%2Dlearning+framework+for+geometry+problem+solving+using+reinforcement+learning+in+deductive+reasoning%2E+In+Findings+of+the)
181. Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020. [Exploring the limits of transfer learning with a unified text-to-text transformer](https://md.research.elamur.ai/free/en/papers/raffel-2019-exploring-limits-transfer-learning-unified/). Journal of machine learning research, 21(140):1–67.
182. Yingzhe Peng, Gongrui Zhang, Miaosen Zhang, Zhiyuan You, Jie Liu, Qipeng Zhu, Kai Yang, Xingzhong Xu, Xin Geng, and Xu Yang. 2025. Lmm-r1: Empowering 3b lmms with strong reasoning abilities through two-stage rule-based rl. arXiv preprint arXiv:2503.07536. — [arXiv:2503.07536](https://arxiv.org/abs/2503.07536) [find](https://scholar.google.com/scholar?q=Yingzhe+Peng%2C+Gongrui+Zhang%2C+Miaosen+Zhang%2C+Zhiyuan+You%2C+Jie+Liu%2C+Qipeng+Zhu%2C+Kai+Yang%2C+Xingzhong+Xu%2C+Xin+Geng%2C+and+Xu+Yang%2E+2025%2E+Lmm%2Dr1%3A+Empowering+3b+lmms+with+strong+reasoning+abilities+through+tw)
183. Andrea Raffo, Andrea Ranieri, Chiara Romanengo, Bianca Falcidieno, and Silvia Biasotti. 2024. Curveml: a benchmark for evaluating and training learning-based methods of classification, recognition, and fitting of plane curves. The Visual Computer, pages 1–21. [find](https://scholar.google.com/scholar?q=Andrea+Raffo%2C+Andrea+Ranieri%2C+Chiara+Romanengo%2C+Bianca+Falcidieno%2C+and+Silvia+Biasotti%2E+2024%2E+Curveml%3A+a+benchmark+for+evaluating+and+training+learning%2Dbased+methods+of+classification%2C+recognition%2C+an)
184. Ji Qi, Ming Ding, Weihan Wang, Yushi Bai, Qingsong Lv, Wenyi Hong, Bin Xu, Lei Hou, Juanzi Li, Yuxiao Dong, and Jie Tang. 2025. Cogcom: A visual language model with chain-of-manipulations reasoning. In The Thirteenth International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Ji+Qi%2C+Ming+Ding%2C+Weihan+Wang%2C+Yushi+Bai%2C+Qingsong+Lv%2C+Wenyi+Hong%2C+Bin+Xu%2C+Lei+Hou%2C+Juanzi+Li%2C+Yuxiao+Dong%2C+and+Jie+Tang%2E+2025%2E+Cogcom%3A+A+visual+language+model+with+chain%2Dof%2Dmanipulations+reasoning%2E+I)
185. Yongsheng Rao, Lanxing Xie, Hao Guan, Jing Li, and Qixin Zhou. 2022. A method for expanding predicates and rules in automated geometry reasoning system. Mathematics, 10(7):1177. [find](https://scholar.google.com/scholar?q=Yongsheng+Rao%2C+Lanxing+Xie%2C+Hao+Guan%2C+Jing+Li%2C+and+Qixin+Zhou%2E+2022%2E+A+method+for+expanding+predicates+and+rules+in+automated+geometry+reasoning+system%2E+Mathematics%2C+10%287%29%3A1177%2E)
186. Cheng Qian, Chi Han, Yi Fung, Yujia Qin, Zhiyuan Liu, and Heng Ji. 2023. [Creator: Tool creation for disentangling abstract and concrete reasoning of large language models](https://md.research.elamur.ai/free/en/papers/qian-2023-creator-tool-creation-disentangling-abstract/). In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 6922–6939.
187. Runqi Qiao, Qiuna Tan, Guanting Dong, MinhuiWu MinhuiWu, Chong Sun, Xiaoshuai Song, Jiapeng Wang, Zhuoma Gongque, Shanglin Lei, Yifan Zhang, and 1 others. 2025. We-math: Does your large multimodal model achieve human-like mathematical reasoning? In Proceedings of the 63rd Annual Meeting of the Assoc… [find](https://scholar.google.com/scholar?q=Runqi+Qiao%2C+Qiuna+Tan%2C+Guanting+Dong%2C+MinhuiWu+MinhuiWu%2C+Chong+Sun%2C+Xiaoshuai+Song%2C+Jiapeng+Wang%2C+Zhuoma+Gongque%2C+Shanglin+Lei%2C+Yifan+Zhang%2C+and+1+others%2E+2025%2E+We%2Dmath%3A+Does+your+large+multimodal+mod)
188. Jingwei Qu, Xiaoqing Lu, Songping Fu, and Zhi Tang. 2016. Improving pgf retrieval effectiveness with active learning. In 2016 23rd International Conference on Pattern Recognition (ICPR), pages 1125–1130. IEEE. [find](https://scholar.google.com/scholar?q=Jingwei+Qu%2C+Xiaoqing+Lu%2C+Songping+Fu%2C+and+Zhi+Tang%2E+2016%2E+Improving+pgf+retrieval+effectiveness+with+active+learning%2E+In+2016+23rd+International+Conference+on+Pattern+Recognition+%28ICPR%29%2C+pages+1125%E2%80%9311)
189. Mrinmaya Sachan, Avinava Dubey, Eduard H Hovy, Tom M Mitchell, Dan Roth, and Eric P Xing. 2019. Discourse in multimedia: A case study in extracting geometry knowledge from textbooks. Computational Linguistics, 45(4):627–665. [find](https://scholar.google.com/scholar?q=Mrinmaya+Sachan%2C+Avinava+Dubey%2C+Eduard+H+Hovy%2C+Tom+M+Mitchell%2C+Dan+Roth%2C+and+Eric+P+Xing%2E+2019%2E+Discourse+in+multimedia%3A+A+case+study+in+extracting+geometry+knowledge+from+textbooks%2E+Computational+Lin)
190. Mrinmaya Sachan, Kumar Dubey, and Eric Xing. 2017. From textbooks to knowledge: A case study in harvesting axiomatic knowledge from textbooks to solve geometry problems. In Proceedings of the 2017 conference on empirical methods in natural language processing, pages 773–784. [find](https://scholar.google.com/scholar?q=Mrinmaya+Sachan%2C+Kumar+Dubey%2C+and+Eric+Xing%2E+2017%2E+From+textbooks+to+knowledge%3A+A+case+study+in+harvesting+axiomatic+knowledge+from+textbooks+to+solve+geometry+problems%2E+In+Proceedings+of+the+2017+con)
191. Mrinmaya Sachan and Eric Xing. 2017. Learning to solve geometry problems from natural language demonstrations in textbooks. In Proceedings of the 6th joint conference on lexical and computational semantics (* SEM 2017), pages 251–261. [find](https://scholar.google.com/scholar?q=Mrinmaya+Sachan+and+Eric+Xing%2E+2017%2E+Learning+to+solve+geometry+problems+from+natural+language+demonstrations+in+textbooks%2E+In+Proceedings+of+the+6th+joint+conference+on+lexical+and+computational+sema)
192. V Sanh. 2019. Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter. In Proceedings of Thirty-third Conference on Neural Information Processing Systems (NIPS2019). [find](https://scholar.google.com/scholar?q=V+Sanh%2E+2019%2E+Distilbert%2C+a+distilled+version+of+bert%3A+smaller%2C+faster%2C+cheaper+and+lighter%2E+In+Proceedings+of+Thirty%2Dthird+Conference+on+Neural+Information+Processing+Systems+%28NIPS2019%29%2E)
193. Amrutesh Saraf, Pooja Kamat, Shilpa Gite, Satish Kumar, and Ketan Kotecha. 2024. Towards robust automated math problem solving: a survey of statistical and deep learning approaches. Evolutionary Intelligence, 17(5):3113–3150. [find](https://scholar.google.com/scholar?q=Amrutesh+Saraf%2C+Pooja+Kamat%2C+Shilpa+Gite%2C+Satish+Kumar%2C+and+Ketan+Kotecha%2E+2024%2E+Towards+robust+automated+math+problem+solving%3A+a+survey+of+statistical+and+deep+learning+approaches%2E+Evolutionary+Intel)
194. SBSC: step-by-step coding for improving mathematical olympiad performance. In The Thirteenth International Conference on Learning Representations, ICLR 2025, Singapore, April 24-28, 2025. OpenReview.net. [find](https://scholar.google.com/scholar?q=SBSC%3A+step%2Dby%2Dstep+coding+for+improving+mathematical+olympiad+performance%2E+In+The+Thirteenth+International+Conference+on+Learning+Representations%2C+ICLR+2025%2C+Singapore%2C+April+24%2D28%2C+2025%2E+OpenReview%2En)
195. Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. 2008. The graph neural network model. IEEE transactions on neural networks, 20(1):61–80. [find](https://scholar.google.com/scholar?q=Franco+Scarselli%2C+Marco+Gori%2C+Ah+Chung+Tsoi%2C+Markus+Hagenbuchner%2C+and+Gabriele+Monfardini%2E+2008%2E+The+graph+neural+network+model%2E+IEEE+transactions+on+neural+networks%2C+20%281%29%3A61%E2%80%9380%2E)
196. Shiven Sinha, Ameya Prabhu, Ponnurangam Kumaraguru, Siddharth Bhat, and Matthias Bethge. Wu’s method boosts symbolic ai to rival silver medalists and alphageometry to outperform gold medalists at imo geometry. In The 4th Workshop on Mathematical Reasoning and AI at NeurIPS’24. [find](https://scholar.google.com/scholar?q=Shiven+Sinha%2C+Ameya+Prabhu%2C+Ponnurangam+Kumaraguru%2C+Siddharth+Bhat%2C+and+Matthias+Bethge%2E+Wu%E2%80%99s+method+boosts+symbolic+ai+to+rival+silver+medalists+and+alphageometry+to+outperform+gold+medalists+at+imo+)
197. Daniel Scher. 1999. Lifting the curtain: The evolution of the geometer’s sketchpad. The Mathematics Educator, 10(2). [find](https://scholar.google.com/scholar?q=Daniel+Scher%2E+1999%2E+Lifting+the+curtain%3A+The+evolution+of+the+geometer%E2%80%99s+sketchpad%2E+The+Mathematics+Educator%2C+10%282%29%2E)
198. John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347. [find](https://scholar.google.com/scholar?q=John+Schulman%2C+Filip+Wolski%2C+Prafulla+Dhariwal%2C+Alec+Radford%2C+and+Oleg+Klimov%2E+2017%2E+Proximal+policy+optimization+algorithms%2E+arXiv+preprint+arXiv%3A1707%2E06347%2E)
199. Minjoon Seo, Hannaneh Hajishirzi, Ali Farhadi, Oren Etzioni, and Clint Malcolm. 2015. [Solving geometry problems: Combining text and diagram interpretation](https://md.research.elamur.ai/free/en/papers/seo-2015-solving-geometry-problems-combining-text/). In Proceedings of the 2015 conference on empirical methods in natural language processing, pages 1466– 1476.
200. Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Y Wu, and 1 others. 2024. [Deepseekmath: Pushing the limits of mathematical reasoning in open language models](https://md.research.elamur.ai/free/en/papers/shao-2024-deepseekmath-pushing-limits-mathematical-reasoning/). arXiv preprint arXiv:2402.03300.
201. Aditya Sharma, Aman Dalmia, Mehran Kazemi, Amal Zouaq, and Christopher Pal. 2025. GeoCoder: Solving geometry problems by generating modular code through vision-language models. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 7340–7356, Albuquerque, New Mexico. Associa… [find](https://scholar.google.com/scholar?q=Aditya+Sharma%2C+Aman+Dalmia%2C+Mehran+Kazemi%2C+Amal+Zouaq%2C+and+Christopher+Pal%2E+2025%2E+GeoCoder%3A+Solving+geometry+problems+by+generating+modular+code+through+vision%2Dlanguage+models%2E+In+Findings+of+the+Asso)
202. Yan Shengyuan and Zhong Xiuqin. 2024. Geo-qwen: A geometry problem-solving method based on generative large language models and heuristic reasoning. In 2024 21st International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), pages 1–9. IEEE. [find](https://scholar.google.com/scholar?q=Yan+Shengyuan+and+Zhong+Xiuqin%2E+2024%2E+Geo%2Dqwen%3A+A+geometry+problem%2Dsolving+method+based+on+generative+large+language+models+and+heuristic+reasoning%2E+In+2024+21st+International+Computer+Conference+on+W)
203. Wenhao Shi, Zhiqiang Hu, Yi Bin, Junhua Liu, Yang Yang, See Kiong Ng, Lidong Bing, and Roy Lee. 2024. [Math-llava: Bootstrapping mathematical reasoning for multimodal large language models](https://md.research.elamur.ai/free/en/papers/shi-2024-math-llava-bootstrapping-mathematical-reasoning/). In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 4663–4680.
204. Ayush Singh, Mansi Gupta, Shivank Garg, Abhinav Kumar, and Vansh Agrawal. 2024. Beyond captioning: Task-specific prompting for improved vlm performance in mathematical reasoning. arXiv preprint arXiv:2410.05928. — [arXiv:2410.05928](https://arxiv.org/abs/2410.05928) [find](https://scholar.google.com/scholar?q=Ayush+Singh%2C+Mansi+Gupta%2C+Shivank+Garg%2C+Abhinav+Kumar%2C+and+Vansh+Agrawal%2E+2024%2E+Beyond+captioning%3A+Task%2Dspecific+prompting+for+improved+vlm+performance+in+mathematical+reasoning%2E+arXiv+preprint+arXiv%3A)
205. Kunal Singh, Ankan Biswas, Sayandeep Bhowmick, Pradeep Moturi, and Siva Kishore Gollapalli. 2025. [find](https://scholar.google.com/scholar?q=Kunal+Singh%2C+Ankan+Biswas%2C+Sayandeep+Bhowmick%2C+Pradeep+Moturi%2C+and+Siva+Kishore+Gollapalli%2E+2025%2E)
206. Bing Song, Gang Xiong, Zhen Shen, Fenghua Zhu, Yisheng Lv, and Peijun Ye. 2023. Geometry problem solving based on counter-factual evolutionary reasoning. In 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE), pages 1–6. IEEE. [find](https://scholar.google.com/scholar?q=Bing+Song%2C+Gang+Xiong%2C+Zhen+Shen%2C+Fenghua+Zhu%2C+Yisheng+Lv%2C+and+Peijun+Ye%2E+2023%2E+Geometry+problem+solving+based+on+counter%2Dfactual+evolutionary+reasoning%2E+In+2023+IEEE+19th+International+Conference+on+)
207. Dan Song, Dongming Wang, and Xiaoyu Chen. 2017. Retrieving geometric information from images: the case of hand-drawn diagrams. Data Mining and Knowledge Discovery, 31(4):934–971. [find](https://scholar.google.com/scholar?q=Dan+Song%2C+Dongming+Wang%2C+and+Xiaoyu+Chen%2E+2017%2E+Retrieving+geometric+information+from+images%3A+the+case+of+hand%2Ddrawn+diagrams%2E+Data+Mining+and+Knowledge+Discovery%2C+31%284%29%3A934%E2%80%93971%2E)
208. Dan Song, Qiong Yao, and Junjie Lu. 2020. A novel geometric information retrieval tool for images of geometric diagrams. In 2020 International Conference on Information Science and Education (ICISE-IE), pages 403–411. IEEE. [find](https://scholar.google.com/scholar?q=Dan+Song%2C+Qiong+Yao%2C+and+Junjie+Lu%2E+2020%2E+A+novel+geometric+information+retrieval+tool+for+images+of+geometric+diagrams%2E+In+2020+International+Conference+on+Information+Science+and+Education+%28ICISE%2DIE)
209. Wei Song, Yadong Li, Jianhua Xu, Guowei Wu, Lingfeng Ming, Kexin Yi, Weihua Luo, Houyi Li, Yi Du, Fangda Guo, and 1 others. 2024. M3gia: A cognition inspired multilingual and multimodal general intelligence ability benchmark. arXiv preprint arXiv:2406.05343. — [arXiv:2406.05343](https://arxiv.org/abs/2406.05343) [find](https://scholar.google.com/scholar?q=Wei+Song%2C+Yadong+Li%2C+Jianhua+Xu%2C+Guowei+Wu%2C+Lingfeng+Ming%2C+Kexin+Yi%2C+Weihua+Luo%2C+Houyi+Li%2C+Yi+Du%2C+Fangda+Guo%2C+and+1+others%2E+2024%2E+M3gia%3A+A+cognition+inspired+multilingual+and+multimodal+general+intell)
210. Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, and 1 others. 2022. [Beyond the imitation game: Quantifying and extrapolating the capabilities of language models](https://md.research.elamur.ai/free/en/papers/srivastava-2022-imitation-game-quantifying-extrapolating-capabilities/). TRANSACTIONS ON MACHINE LE…
211. Saurabh Srivastava, Anto PV, Shashank Menon, Ajay Sukumar, Alan Philipose, Stevin Prince, Sooraj Thomas, and 1 others. 2024. Functional benchmarks for robust evaluation of reasoning performance, and the reasoning gap. arXiv preprint arXiv:2402.19450. [find](https://scholar.google.com/scholar?q=Saurabh+Srivastava%2C+Anto+PV%2C+Shashank+Menon%2C+Ajay+Sukumar%2C+Alan+Philipose%2C+Stevin+Prince%2C+Sooraj+Thomas%2C+and+1+others%2E+2024%2E+Functional+benchmarks+for+robust+evaluation+of+reasoning+performance%2C+and+t)
212. Haoxiang Sun, Yingqian Min, Zhipeng Chen, Wayne Xin Zhao, Zheng Liu, Zhongyuan Wang, Lei Fang, and Ji-Rong Wen. 2025a. [Challenging the boundaries of reasoning: An olympiad-level math benchmark for large language models](https://md.research.elamur.ai/free/en/papers/sun-2026-challenging-boundaries-reasoning-olympiad-level/). arXiv preprint arXiv:2503.21380.
213. Kai Sun, Yushi Bai, Ji Qi, Lei Hou, and Juanzi Li. 2024. Mm-math: Advancing multimodal math evaluation with process evaluation and fine-grained classification. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 1358–1375. [find](https://scholar.google.com/scholar?q=Kai+Sun%2C+Yushi+Bai%2C+Ji+Qi%2C+Lei+Hou%2C+and+Juanzi+Li%2E+2024%2E+Mm%2Dmath%3A+Advancing+multimodal+math+evaluation+with+process+evaluation+and+fine%2Dgrained+classification%2E+In+Findings+of+the+Association+for+Compu)
214. Linzhuang Sun, Hao Liang, Jingxuan Wei, Bihui Yu, Tianpeng Li, Fan Yang, Zenan Zhou, and Wentao Zhang. 2025b. [Mm-verify: Enhancing multimodal reasoning with chain-of-thought verification](https://md.research.elamur.ai/free/en/papers/sun-2025-mm-verify-enhancing-multimodal-reasoning/). In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Paper…
215. Qwen Team, An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, Huan Lin, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang, Jiaxi Yang, Jingren Zhou, Junyang Lin, and 24 others. 2025. Qwen2.5 technical report. Preprint, arXiv:24… [find](https://scholar.google.com/scholar?q=Qwen+Team%2C+An+Yang%2C+Baosong+Yang%2C+Beichen+Zhang%2C+Binyuan+Hui%2C+Bo+Zheng%2C+Bowen+Yu%2C+Chengyuan+Li%2C+Dayiheng+Liu%2C+Fei+Huang%2C+Haoran+Wei%2C+Huan+Lin%2C+Jian+Yang%2C+Jianhong+Tu%2C+Jianwei+Zhang%2C+Jianxin+Yang%2C+Jiax)
216. Ruiyong Sun, Yijia Zhao, Qi Zhang, Keyu Ding, Shijin Wang, and Cui Wei. 2019. A neural semantic parser for math problems incorporating multi-sentence information. ACM Transactions on Asian and Low-Resource Language Information Processing (TAL-LIP), 18(4):1–16. [find](https://scholar.google.com/scholar?q=Ruiyong+Sun%2C+Yijia+Zhao%2C+Qi+Zhang%2C+Keyu+Ding%2C+Shijin+Wang%2C+and+Cui+Wei%2E+2019%2E+A+neural+semantic+parser+for+math+problems+incorporating+multi%2Dsentence+information%2E+ACM+Transactions+on+Asian+and+Low%2DRes)
217. Joseph Tey. Understanding how vision-language models reason when solving visual math problems. [find](https://scholar.google.com/scholar?q=Joseph+Tey%2E+Understanding+how+vision%2Dlanguage+models+reason+when+solving+visual+math+problems%2E)
218. Yanpeng Sun, Shan Zhang, Wei Tang, Aotian Chen, Piotr Koniusz, Kai Zou, Yuan Xue, and Anton van den Hengel. 2026. Math blind: Failures in diagram understanding undermine reasoning in MLLMs. In The Fourteenth International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Yanpeng+Sun%2C+Shan+Zhang%2C+Wei+Tang%2C+Aotian+Chen%2C+Piotr+Koniusz%2C+Kai+Zou%2C+Yuan+Xue%2C+and+Anton+van+den+Hengel%2E+2026%2E+Math+blind%3A+Failures+in+diagram+understanding+undermine+reasoning+in+MLLMs%2E+In+The+Fou)
219. Yiyou Sun, Georgia Zhou, Haoyue Bai, Hao Wang, Dacheng Li, Nouha Dziri, and Dawn Song. 2025c. Climbing the ladder of reasoning: What llms can—and still can’t—solve after sft? In The 5th Workshop on Mathematical Reasoning and AI at NeurIPS 2025. [find](https://scholar.google.com/scholar?q=Yiyou+Sun%2C+Georgia+Zhou%2C+Haoyue+Bai%2C+Hao+Wang%2C+Dacheng+Li%2C+Nouha+Dziri%2C+and+Dawn+Song%2E+2025c%2E+Climbing+the+ladder+of+reasoning%3A+What+llms+can%E2%80%94and+still+can%E2%80%99t%E2%80%94solve+after+sft%3F+In+The+5th+Workshop+on+Ma)
220. Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014. [Sequence to sequence learning with neural networks](https://md.research.elamur.ai/free/en/papers/sutskever-2014-sequence-sequence-learning-neural-networks/). Advances in neural information processing systems, 27.
221. Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc Le, Ed Chi, Denny Zhou, and 1 others. 2023. [Challenging big-bench tasks and whether chain-of-thought can solve them](https://md.research.elamur.ai/free/en/papers/suzgun-2023-challenging-big-bench-tasks-whether/). In Findings of the Association for Computational Linguistics: AC…
222. Huajie Tan, Yuheng Ji, Xiaoshuai Hao, Xiansheng Chen, Pengwei Wang, Zhongyuan Wang, and Shanghang Zhang. 2025. Reason-rft: Reinforcement fine-tuning for visual reasoning of vision language models. In The Thirty-ninth Annual Conference on Neural Information Processing Systems. [find](https://scholar.google.com/scholar?q=Huajie+Tan%2C+Yuheng+Ji%2C+Xiaoshuai+Hao%2C+Xiansheng+Chen%2C+Pengwei+Wang%2C+Zhongyuan+Wang%2C+and+Shanghang+Zhang%2E+2025%2E+Reason%2Drft%3A+Reinforcement+fine%2Dtuning+for+visual+reasoning+of+vision+language+models%2E+In+)
223. Jiamin Tang, Chao Zhang, Xudong Zhu, and Mengchi Liu. 2025. Geoer: A challenging benchmark for geometric element recognition. In International Conference on Knowledge Science, Engineering and Management, pages 266–273. Springer. [find](https://scholar.google.com/scholar?q=Jiamin+Tang%2C+Chao+Zhang%2C+Xudong+Zhu%2C+and+Mengchi+Liu%2E+2025%2E+Geoer%3A+A+challenging+benchmark+for+geometric+element+recognition%2E+In+International+Conference+on+Knowledge+Science%2C+Engineering+and+Manageme)
224. Zhengyang Tang, Xingxing Zhang, Benyou Wang, and Furu Wei. 2024. Mathscale: Scaling instruction tuning for mathematical reasoning. In International Conference on Machine Learning, pages 47885– 47900. PMLR. [find](https://scholar.google.com/scholar?q=Zhengyang+Tang%2C+Xingxing+Zhang%2C+Benyou+Wang%2C+and+Furu+Wei%2E+2024%2E+Mathscale%3A+Scaling+instruction+tuning+for+mathematical+reasoning%2E+In+International+Conference+on+Machine+Learning%2C+pages+47885%E2%80%93+47900%2E+)
225. Pittawat Taveekitworachai, Febri Abdullah, and Ruck Thawonmas. 2024. Null-shot prompting: rethinking prompting large language models with hallucination. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 13321–13361. [find](https://scholar.google.com/scholar?q=Pittawat+Taveekitworachai%2C+Febri+Abdullah%2C+and+Ruck+Thawonmas%2E+2024%2E+Null%2Dshot+prompting%3A+rethinking+prompting+large+language+models+with+hallucination%2E+In+Proceedings+of+the+2024+Conference+on+Empiri)
226. Yuxuan Tong, Xiwen Zhang, Rui Wang, Ruidong Wu, and Junxian He. 2024. Dart-math: Difficulty-aware rejection tuning for mathematical problem-solving. Advances in Neural Information Processing Systems, 37:7821–7846. [find](https://scholar.google.com/scholar?q=Yuxuan+Tong%2C+Xiwen+Zhang%2C+Rui+Wang%2C+Ruidong+Wu%2C+and+Junxian+He%2E+2024%2E+Dart%2Dmath%3A+Difficulty%2Daware+rejection+tuning+for+mathematical+problem%2Dsolving%2E+Advances+in+Neural+Information+Processing+Systems%2C+)
227. Trieu H Trinh, Yuhuai Wu, Quoc V Le, He He, and Thang Luong. 2024. [Solving olympiad geometry without human demonstrations](https://md.research.elamur.ai/free/en/papers/trinh-2024-solving-olympiad-geometry-without-human/). Nature, 625(7995):476–482.
228. Shih-Hung Tsai, Chao-Chun Liang, Hsin-Min Wang, and Keh-Yih Su. 2021. Sequence to general tree: Knowledge-guided geometry word problem solving. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language P… [find](https://scholar.google.com/scholar?q=Shih%2DHung+Tsai%2C+Chao%2DChun+Liang%2C+Hsin%2DMin+Wang%2C+and+Keh%2DYih+Su%2E+2021%2E+Sequence+to+general+tree%3A+Knowledge%2Dguided+geometry+word+problem+solving%2E+In+Proceedings+of+the+59th+Annual+Meeting+of+the+Associa)
229. George Tsoukalas, Jasper Lee, John Jennings, Jimmy Xin, Michelle Ding, Michael Jennings, Amitayush Thakur, and Swarat Chaudhuri. 2024. Putnambench: Evaluating neural theorem-provers on the putnam mathematical competition. In The Thirty-eight Conference on Neural Information Processing Systems Datase… [find](https://scholar.google.com/scholar?q=George+Tsoukalas%2C+Jasper+Lee%2C+John+Jennings%2C+Jimmy+Xin%2C+Michelle+Ding%2C+Michael+Jennings%2C+Amitayush+Thakur%2C+and+Swarat+Chaudhuri%2E+2024%2E+Putnambench%3A+Evaluating+neural+theorem%2Dprovers+on+the+putnam+math)
230. Haoqin Tu, Weitao Feng, Hardy Chen, Hui Liu, Xianfeng Tang, and Cihang Xie. 2025. [Vilbench: A suite for vision-language process reward modeling](https://md.research.elamur.ai/free/en/papers/tu-2025-vilbench-suite-vision-language-process/). In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 6775–6790.
231. Aaron Van Den Oord, Oriol Vinyals, and 1 others. 2017. Neural discrete representation learning. Advances in neural information processing systems, 30. [find](https://scholar.google.com/scholar?q=Aaron+Van+Den+Oord%2C+Oriol+Vinyals%2C+and+1+others%2E+2017%2E+Neural+discrete+representation+learning%2E+Advances+in+neural+information+processing+systems%2C+30%2E)
232. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. [Attention is all you need](https://md.research.elamur.ai/free/en/papers/vaswani-2017-attention-all-need/). Advances in neural information processing systems, 30.
233. Duc Anh Vu, Cong-Duy Nguyen, Xiaobao Wu, Nhat Hoang, Mingzhe Du, Thong Nguyen, and Anh Tuan Luu. 2025. Curriculum demonstration selection for in-context learning. In Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing, pages 1004–1006. [find](https://scholar.google.com/scholar?q=Duc+Anh+Vu%2C+Cong%2DDuy+Nguyen%2C+Xiaobao+Wu%2C+Nhat+Hoang%2C+Mingzhe+Du%2C+Thong+Nguyen%2C+and+Anh+Tuan+Luu%2E+2025%2E+Curriculum+demonstration+selection+for+in%2Dcontext+learning%2E+In+Proceedings+of+the+40th+ACM%2FSIGAPP)
234. Junxiao Wang, Ting Zhang, Heng Yu, Jingdong Wang, and Hua Huang. 2025a. Magicgeo: Training-free text-guided geometric diagram generation. arXiv preprint arXiv:2502.13855. — [arXiv:2502.13855](https://arxiv.org/abs/2502.13855) [find](https://scholar.google.com/scholar?q=Junxiao+Wang%2C+Ting+Zhang%2C+Heng+Yu%2C+Jingdong+Wang%2C+and+Hua+Huang%2E+2025a%2E+Magicgeo%3A+Training%2Dfree+text%2Dguided+geometric+diagram+generation%2E+arXiv+preprint+arXiv%3A2502%2E13855%2E+%E2%80%94+%5BarXiv%3A2502%2E13855%5D%28https%3A%2F%2F)
235. Ke Wang, Junting Pan, Weikang Shi, Zimu Lu, Houxing Ren, Aojun Zhou, Mingjie Zhan, and Hongsheng Li. 2024a. [Measuring multimodal mathematical reasoning with math-vision dataset](https://md.research.elamur.ai/free/en/papers/wang-2024-measuring-multimodal-mathematical-reasoning-math/). Advances in Neural Information Processing Systems, 37:95095–95169.
236. Kuan Wang, Jianfeng Gao, and 1 others. 2025i. Reinforcement learning for reasoning in large language models with one training example. In The Thirty-ninth Annual Conference on Neural Information Processing Systems. [find](https://scholar.google.com/scholar?q=Kuan+Wang%2C+Jianfeng+Gao%2C+and+1+others%2E+2025i%2E+Reinforcement+learning+for+reasoning+in+large+language+models+with+one+training+example%2E+In+The+Thirty%2Dninth+Annual+Conference+on+Neural+Information+Proce)
237. Lei Wang, Wanyu Xu, Zhiqiang Hu, Yihuai Lan, Shan Dong, Hao Wang, Roy Ka-Wei Lee, and Ee-Peng Lim. 2024b. All in an aggregated image for in-image learning. arXiv preprint arXiv:2402.17971. — [arXiv:2402.17971](https://arxiv.org/abs/2402.17971) [find](https://scholar.google.com/scholar?q=Lei+Wang%2C+Wanyu+Xu%2C+Zhiqiang+Hu%2C+Yihuai+Lan%2C+Shan+Dong%2C+Hao+Wang%2C+Roy+Ka%2DWei+Lee%2C+and+Ee%2DPeng+Lim%2E+2024b%2E+All+in+an+aggregated+image+for+in%2Dimage+learning%2E+arXiv+preprint+arXiv%3A2402%2E17971%2E+%E2%80%94+%5BarXiv%3A24)
238. Zhikai Wang, Jiashuo Sun, Wenqi Zhang, Zhiqiang Hu, Xin Li, Fan Wang, and Deli Zhao. 2025j. Benchmarking multimodal mathematical reasoning with explicit visual dependency. arXiv preprint arXiv:2504.18589. — [arXiv:2504.18589](https://arxiv.org/abs/2504.18589) [find](https://scholar.google.com/scholar?q=Zhikai+Wang%2C+Jiashuo+Sun%2C+Wenqi+Zhang%2C+Zhiqiang+Hu%2C+Xin+Li%2C+Fan+Wang%2C+and+Deli+Zhao%2E+2025j%2E+Benchmarking+multimodal+mathematical+reasoning+with+explicit+visual+dependency%2E+arXiv+preprint+arXiv%3A2504%2E18)
239. Long Wang and Jianhui Ma. 2024. Mutli-step chain-of-thought in geometry problem solving. In 2024 4th International Conference on Electronic Information Engineering and Computer Science (EIECS), pages 1113–1117. IEEE. [find](https://scholar.google.com/scholar?q=Long+Wang+and+Jianhui+Ma%2E+2024%2E+Mutli%2Dstep+chain%2Dof%2Dthought+in+geometry+problem+solving%2E+In+2024+4th+International+Conference+on+Electronic+Information+Engineering+and+Computer+Science+%28EIECS%29%2C+pages+)
240. Peijie Wang, Zhong-Zhi Li, Fei Yin, Dekang Ran, and Cheng-Lin Liu. 2025b. Mv-math: Evaluating multimodal math reasoning in multi-visual contexts. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 19541–19551. [find](https://scholar.google.com/scholar?q=Peijie+Wang%2C+Zhong%2DZhi+Li%2C+Fei+Yin%2C+Dekang+Ran%2C+and+Cheng%2DLin+Liu%2E+2025b%2E+Mv%2Dmath%3A+Evaluating+multimodal+math+reasoning+in+multi%2Dvisual+contexts%2E+In+Proceedings+of+the+Computer+Vision+and+Pattern+Reco)
241. Weiyun Wang, Zhangwei Gao, Lianjie Chen, Zhe Chen, Jinguo Zhu, Xiangyu Zhao, Yangzhou Liu, Yue Cao, Shenglong Ye, Xizhou Zhu, and 1 others. 2025c. Visualprm: An effective process reward model for multimodal reasoning. arXiv preprint arXiv:2503.10291. — [arXiv:2503.10291](https://arxiv.org/abs/2503.10291) [find](https://scholar.google.com/scholar?q=Weiyun+Wang%2C+Zhangwei+Gao%2C+Lianjie+Chen%2C+Zhe+Chen%2C+Jinguo+Zhu%2C+Xiangyu+Zhao%2C+Yangzhou+Liu%2C+Yue+Cao%2C+Shenglong+Ye%2C+Xizhou+Zhu%2C+and+1+others%2E+2025c%2E+Visualprm%3A+An+effective+process+reward+model+for+mult)
242. Xiaofeng Wang, Yiming Wang, Wenhong Zhu, and Rui Wang. 2025d. Do large language models truly understand geometric structures? In The Thirteenth International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Xiaofeng+Wang%2C+Yiming+Wang%2C+Wenhong+Zhu%2C+and+Rui+Wang%2E+2025d%2E+Do+large+language+models+truly+understand+geometric+structures%3F+In+The+Thirteenth+International+Conference+on+Learning+Representations%2E)
243. Xiyao Wang, Zhengyuan Yang, Chao Feng, Hongjin Lu, Linjie Li, Chung-Ching Lin, Kevin Lin, Furong Huang, and Lijuan Wang. 2025e. Sota with less: Mcts-guided sample selection for data-efficient visual reasoning self-improvement. In The Thirty-ninth Annual Conference on Neural Information Processing Sy… [find](https://scholar.google.com/scholar?q=Xiyao+Wang%2C+Zhengyuan+Yang%2C+Chao+Feng%2C+Hongjin+Lu%2C+Linjie+Li%2C+Chung%2DChing+Lin%2C+Kevin+Lin%2C+Furong+Huang%2C+and+Lijuan+Wang%2E+2025e%2E+Sota+with+less%3A+Mcts%2Dguided+sample+selection+for+data%2Defficient+visual+r)
244. Yikun Wang, Siyin Wang, Qinyuan Cheng, Zhaoye Fei, Liang Ding, Qipeng Guo, Dacheng Tao, and Xipeng Qiu. 2025f. [Visuothink: Empowering lvlm reasoning with multimodal tree search](https://md.research.elamur.ai/free/en/papers/wang-2025-visuothink-empowering-lvlm-reasoning-multimodal/). In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages…
245. Yiming Wang, Pei Zhang, Jialong Tang, Hao-Ran Wei, Baosong Yang, Rui Wang, Chenshu Sun, Feitong Sun, Jiran Zhang, Junxuan Wu, and 1 others. 2025g. Polymath: Evaluating mathematical reasoning in multilingual contexts. In The Thirty-ninth Annual Conference on Neural Information Processing Systems Data… [find](https://scholar.google.com/scholar?q=Yiming+Wang%2C+Pei+Zhang%2C+Jialong+Tang%2C+Hao%2DRan+Wei%2C+Baosong+Yang%2C+Rui+Wang%2C+Chenshu+Sun%2C+Feitong+Sun%2C+Jiran+Zhang%2C+Junxuan+Wu%2C+and+1+others%2E+2025g%2E+Polymath%3A+Evaluating+mathematical+reasoning+in+multil)
246. Ying Wang, Wei Zhou, Yongsheng Rao, and Hao Guan. 2025h. A knowledge and semantic fusion method for automatic geometry problem understanding. Applied Sciences, 15(7):3857. [find](https://scholar.google.com/scholar?q=Ying+Wang%2C+Wei+Zhou%2C+Yongsheng+Rao%2C+and+Hao+Guan%2E+2025h%2E+A+knowledge+and+semantic+fusion+method+for+automatic+geometry+problem+understanding%2E+Applied+Sciences%2C+15%287%29%3A3857%2E)
247. Yiping Wang, Qing Yang, Zhiyuan Zeng, Liliang Ren, Liyuan Liu, Baolin Peng, Hao Cheng, Xuehai He, Haoran Wei, Youyang Yin, Yumeng Li, Jia Wang, Liang Zhao, Jianjian Sun, Zheng Ge, Xiangyu Zhang, and Daxin Jiang. 2024. Slow perception: Let’s perceive geometric figures step-by-step. arXiv preprint arX… — [arXiv:2412.20631](https://arxiv.org/abs/2412.20631) [find](https://scholar.google.com/scholar?q=Yiping+Wang%2C+Qing+Yang%2C+Zhiyuan+Zeng%2C+Liliang+Ren%2C+Liyuan+Liu%2C+Baolin+Peng%2C+Hao+Cheng%2C+Xuehai+He%2C+Haoran+Wei%2C+Youyang+Yin%2C+Yumeng+Li%2C+Jia+Wang%2C+Liang+Zhao%2C+Jianjian+Sun%2C+Zheng+Ge%2C+Xiangyu+Zhang%2C+and+D)
248. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, and 1 others. 2022. [Chain-of-thought prompting elicits reasoning in large language models](https://md.research.elamur.ai/free/en/papers/wei-2022-chain-thought-prompting-elicits-reasoning/). Advances in neural information processing systems, 35:24824– 24837.
249. Zixin Wen, Yifu Cai, Kyle Lee, Sam Estep, Joshua Sunshine, Aarti Singh, Yuejie Chi, and Wode Ni. 2025. Feynman: Knowledge-infused diagramming agent for scaling visual reasoning data. [find](https://scholar.google.com/scholar?q=Zixin+Wen%2C+Yifu+Cai%2C+Kyle+Lee%2C+Sam+Estep%2C+Joshua+Sunshine%2C+Aarti+Singh%2C+Yuejie+Chi%2C+and+Wode+Ni%2E+2025%2E+Feynman%3A+Knowledge%2Dinfused+diagramming+agent+for+scaling+visual+reasoning+data%2E)
250. Tengjin Weng, Jingyi Wang, Wenhao Jiang, and Zhong Ming. 2025. Visnumbench: Evaluating number sense of multimodal large language models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3830–3840. [find](https://scholar.google.com/scholar?q=Tengjin+Weng%2C+Jingyi+Wang%2C+Wenhao+Jiang%2C+and+Zhong+Ming%2E+2025%2E+Visnumbench%3A+Evaluating+number+sense+of+multimodal+large+language+models%2E+In+Proceedings+of+the+IEEE%2FCVF+International+Conference+on+Comp)
251. Man Fai Wong, Xintong Qi, and Chee Wei Tan. 2023. Euclidnet: Deep visual reasoning for constructible problems in geometry. Advances in Artificial Intelligence and Machine Learning, 3(1):839–853. [find](https://scholar.google.com/scholar?q=Man+Fai+Wong%2C+Xintong+Qi%2C+and+Chee+Wei+Tan%2E+2023%2E+Euclidnet%3A+Deep+visual+reasoning+for+constructible+problems+in+geometry%2E+Advances+in+Artificial+Intelligence+and+Machine+Learning%2C+3%281%29%3A839%E2%80%93853%2E)
252. Haoyi Wu, Wenyang Hui, Yezeng Chen, Weiqi Wu, Kewei Tu, and Yi Zhou. 2023. Conic10k: A challenging math problem understanding and reasoning dataset. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 6444–6458. [find](https://scholar.google.com/scholar?q=Haoyi+Wu%2C+Wenyang+Hui%2C+Yezeng+Chen%2C+Weiqi+Wu%2C+Kewei+Tu%2C+and+Yi+Zhou%2E+2023%2E+Conic10k%3A+A+challenging+math+problem+understanding+and+reasoning+dataset%2E+In+Findings+of+the+Association+for+Computational+Li)
253. Jinyang Wu, Mingkuan Feng, Shuai Zhang, Ruihan Jin, Feihu Che, Zengqi Wen, and Jianhua Tao. 2025. Boosting multimodal reasoning with mcts-automated structured thinking. arXiv preprint arXiv:2502.02339. — [arXiv:2502.02339](https://arxiv.org/abs/2502.02339) [find](https://scholar.google.com/scholar?q=Jinyang+Wu%2C+Mingkuan+Feng%2C+Shuai+Zhang%2C+Ruihan+Jin%2C+Feihu+Che%2C+Zengqi+Wen%2C+and+Jianhua+Tao%2E+2025%2E+Boosting+multimodal+reasoning+with+mcts%2Dautomated+structured+thinking%2E+arXiv+preprint+arXiv%3A2502%2E02339)
254. Wenjun Wu, Lingling Zhang, Jun Liu, Xi Tang, Yaxian Wang, Shaowei Wang, and Qianying Wang. 2024a. E-gps: Explainable geometry problem solving via top-down solver and bottom-up generator. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13828–13837. [find](https://scholar.google.com/scholar?q=Wenjun+Wu%2C+Lingling+Zhang%2C+Jun+Liu%2C+Xi+Tang%2C+Yaxian+Wang%2C+Shaowei+Wang%2C+and+Qianying+Wang%2E+2024a%2E+E%2Dgps%3A+Explainable+geometry+problem+solving+via+top%2Ddown+solver+and+bottom%2Dup+generator%2E+In+Proceeding)
255. Yanan Wu, Jie Liu, Xingyuan Bu, Jiaheng Liu, Zhanhui Zhou, Yuanxing Zhang, Chenchen Zhang, ZhiqiBai ZhiqiBai, Haibin Chen, Tiezheng Ge, and 1 others. 2024b. Conceptmath: A bilingual concept-wise benchmark for measuring mathematical reasoning of large language models. In Findings of the Association f… [find](https://scholar.google.com/scholar?q=Yanan+Wu%2C+Jie+Liu%2C+Xingyuan+Bu%2C+Jiaheng+Liu%2C+Zhanhui+Zhou%2C+Yuanxing+Zhang%2C+Chenchen+Zhang%2C+ZhiqiBai+ZhiqiBai%2C+Haibin+Chen%2C+Tiezheng+Ge%2C+and+1+others%2E+2024b%2E+Conceptmath%3A+A+bilingual+concept%2Dwise+bench)
256. Jing Xia and Xinguo Yu. 2021. A paradigm of diagram understanding in problem solving. In 2021 IEEE International Conference on Engineering, Technology & Education (TALE), pages 531–535. IEEE. [find](https://scholar.google.com/scholar?q=Jing+Xia+and+Xinguo+Yu%2E+2021%2E+A+paradigm+of+diagram+understanding+in+problem+solving%2E+In+2021+IEEE+International+Conference+on+Engineering%2C+Technology+%26+Education+%28TALE%29%2C+pages+531%E2%80%93535%2E+IEEE%2E)
257. Haotian Xu, Xing Wu, Weinong Wang, Zhongzhi Li, Da Zheng, Boyuan Chen, Yi Hu, Shijia Kang, Jiaming Ji, Yingying Zhang, and 1 others. 2025b. Redstar: Does scaling long-cot data unlock better slow-reasoning systems? arXiv preprint arXiv:2501.11284. [find](https://scholar.google.com/scholar?q=Haotian+Xu%2C+Xing+Wu%2C+Weinong+Wang%2C+Zhongzhi+Li%2C+Da+Zheng%2C+Boyuan+Chen%2C+Yi+Hu%2C+Shijia+Kang%2C+Jiaming+Ji%2C+Yingying+Zhang%2C+and+1+others%2E+2025b%2E+Redstar%3A+Does+scaling+long%2Dcot+data+unlock+better+slow%2Dreaso)
258. Renqiu Xia, Mingsheng Li, Hancheng Ye, Wenjie Wu, Hongbin Zhou, Jiakang Yuan, Tianshuo Peng, Xinyu Cai, Xiangchao Yan, Bin Wang, Conghui He, Botian Shi, Tao Chen, Junchi Yan, and Bo Zhang. 2025. Geox: Geometric problem solving through unified formalized vision-language pre-training. In The Thirteent… [find](https://scholar.google.com/scholar?q=Renqiu+Xia%2C+Mingsheng+Li%2C+Hancheng+Ye%2C+Wenjie+Wu%2C+Hongbin+Zhou%2C+Jiakang+Yuan%2C+Tianshuo+Peng%2C+Xinyu+Cai%2C+Xiangchao+Yan%2C+Bin+Wang%2C+Conghui+He%2C+Botian+Shi%2C+Tao+Chen%2C+Junchi+Yan%2C+and+Bo+Zhang%2E+2025%2E+Geox%3A)
259. Kun Xiang, Zhili Liu, Zihao Jiang, Yunshuang Nie, Kaixin Cai, Yiyang Yin, Runhui Huang, Haoxiang Fan, Hanhui Li, Weiran Huang, and 1 others. 2025. Can atomic step decomposition enhance the self-structured reasoning of multimodal large models? arXiv preprint arXiv:2503.06252. — [arXiv:2503.06252](https://arxiv.org/abs/2503.06252) [find](https://scholar.google.com/scholar?q=Kun+Xiang%2C+Zhili+Liu%2C+Zihao+Jiang%2C+Yunshuang+Nie%2C+Kaixin+Cai%2C+Yiyang+Yin%2C+Runhui+Huang%2C+Haoxiang+Fan%2C+Hanhui+Li%2C+Weiran+Huang%2C+and+1+others%2E+2025%2E+Can+atomic+step+decomposition+enhance+the+self%2Dstruct)
260. Kun Xiang, Zhili Liu, Terry Jingchen Zhang, Yinya Huang, Yunshuang Nie, Kaixin Cai, Yiyang Yin, Runhui Huang, Hanhui Li, Yihan Zeng, Yu-Jie Yuan, Jianhua Han, Lanqing Hong, Hang Xu, and Xiaodan Liang. 2026. AtomThink: Multimodal Slow Thinking With Atomic Step Reasoning . IEEE Transactions on Pattern… [find](https://scholar.google.com/scholar?q=Kun+Xiang%2C+Zhili+Liu%2C+Terry+Jingchen+Zhang%2C+Yinya+Huang%2C+Yunshuang+Nie%2C+Kaixin+Cai%2C+Yiyang+Yin%2C+Runhui+Huang%2C+Hanhui+Li%2C+Yihan+Zeng%2C+Yu%2DJie+Yuan%2C+Jianhua+Han%2C+Lanqing+Hong%2C+Hang+Xu%2C+and+Xiaodan+Liang%2E)
261. Tong Xiao, Jiayu Liu, Zhenya Huang, Jinze Wu, Jing Sha, Shijin Wang, and Enhong Chen. 2024a. Learning to solve geometry problems via simulating human dual-reasoning process. In Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, pages 6559–6568. [find](https://scholar.google.com/scholar?q=Tong+Xiao%2C+Jiayu+Liu%2C+Zhenya+Huang%2C+Jinze+Wu%2C+Jing+Sha%2C+Shijin+Wang%2C+and+Enhong+Chen%2E+2024a%2E+Learning+to+solve+geometry+problems+via+simulating+human+dual%2Dreasoning+process%2E+In+Proceedings+of+the+Thir)
262. Ziyang Xiao and Dongxiang Zhang. 2023. A deep reinforcement learning agent for geometry online tutoring. Knowledge and Information Systems, 65(4):1611–1625. [find](https://scholar.google.com/scholar?q=Ziyang+Xiao+and+Dongxiang+Zhang%2E+2023%2E+A+deep+reinforcement+learning+agent+for+geometry+online+tutoring%2E+Knowledge+and+Information+Systems%2C+65%284%29%3A1611%E2%80%931625%2E)
263. Ziyang Xiao, Dongxiang Zhang, Xiongwei Han, Xiaojin Fu, Wing Yin Yu, Tao Zhong, Sai Wu, Yuan Wang, Jianwei Yin, and Gang Chen. 2024b. Enhancing llm reasoning via vision-augmented prompting. Advances in Neural Information Processing Systems, 37:28772–28797. [find](https://scholar.google.com/scholar?q=Ziyang+Xiao%2C+Dongxiang+Zhang%2C+Xiongwei+Han%2C+Xiaojin+Fu%2C+Wing+Yin+Yu%2C+Tao+Zhong%2C+Sai+Wu%2C+Yuan+Wang%2C+Jianwei+Yin%2C+and+Gang+Chen%2E+2024b%2E+Enhancing+llm+reasoning+via+vision%2Daugmented+prompting%2E+Advances+i)
264. Shangyu Xing, Changhao Xiang, Yuteng Han, Yifan Yue, Zhen Wu, Xinyu Liu, Zhangtai Wu, Fei Zhao, and Xinyu Dai. 2024. Gepbench: Evaluating fundamental geometric perception for multimodal large language models. arXiv preprint arXiv:2412.21036. — [arXiv:2412.21036](https://arxiv.org/abs/2412.21036) [find](https://scholar.google.com/scholar?q=Shangyu+Xing%2C+Changhao+Xiang%2C+Yuteng+Han%2C+Yifan+Yue%2C+Zhen+Wu%2C+Xinyu+Liu%2C+Zhangtai+Wu%2C+Fei+Zhao%2C+and+Xinyu+Dai%2E+2024%2E+Gepbench%3A+Evaluating+fundamental+geometric+perception+for+multimodal+large+language)
265. Siheng Xiong, Ali Payani, Yuan Yang, and Faramarz Fekri. 2024. Deliberate reasoning for llms as structure-aware planning with accurate world model. arXiv preprint arXiv:2410.03136. — [arXiv:2410.03136](https://arxiv.org/abs/2410.03136) [find](https://scholar.google.com/scholar?q=Siheng+Xiong%2C+Ali+Payani%2C+Yuan+Yang%2C+and+Faramarz+Fekri%2E+2024%2E+Deliberate+reasoning+for+llms+as+structure%2Daware+planning+with+accurate+world+model%2E+arXiv+preprint+arXiv%3A2410%2E03136%2E+%E2%80%94+%5BarXiv%3A2410%2E03136)
266. Guowei Xu, Peng Jin, Ziang Wu, Hao Li, Yibing Song, Lichao Sun, and Li Yuan. 2025a. Llava-cot: Let Liangyu Xu, Yingxiu Zhao, Jingyun Wang, Yingyao Wang, Bu Pi, Chen Wang, Mingliang Zhang, Jihao Gu, Xiang Li, Xiaoyong Zhu, and 1 others. 2025c. Geosense: Evaluating identification and application of ge… — [arXiv:2504.12597](https://arxiv.org/abs/2504.12597) [find](https://scholar.google.com/scholar?q=Guowei+Xu%2C+Peng+Jin%2C+Ziang+Wu%2C+Hao+Li%2C+Yibing+Song%2C+Lichao+Sun%2C+and+Li+Yuan%2E+2025a%2E+Llava%2Dcot%3A+Let+Liangyu+Xu%2C+Yingxiu+Zhao%2C+Jingyun+Wang%2C+Yingyao+Wang%2C+Bu+Pi%2C+Chen+Wang%2C+Mingliang+Zhang%2C+Jihao+Gu%2C+Xi)
267. Shihao Xu, Yiyang Luo, and Wei Shi. 2024a. Geo-llava: A large multi-modal model for solving geometry math problems with meta in-context learning. In Proceedings of the 2nd Workshop on Large Generative Models Meet Multimodal Applications, pages 11–15. [find](https://scholar.google.com/scholar?q=Shihao+Xu%2C+Yiyang+Luo%2C+and+Wei+Shi%2E+2024a%2E+Geo%2Dllava%3A+A+large+multi%2Dmodal+model+for+solving+geometry+math+problems+with+meta+in%2Dcontext+learning%2E+In+Proceedings+of+the+2nd+Workshop+on+Large+Generative)
268. Weijia Xu, Andrzej Banburski, and Nebojsa Jojic. 2024b. Reprompting: Automated chain-of-thought prompt inference through gibbs sampling. In International Conference on Machine Learning, pages 54852–54865. PMLR. [find](https://scholar.google.com/scholar?q=Weijia+Xu%2C+Andrzej+Banburski%2C+and+Nebojsa+Jojic%2E+2024b%2E+Reprompting%3A+Automated+chain%2Dof%2Dthought+prompt+inference+through+gibbs+sampling%2E+In+International+Conference+on+Machine+Learning%2C+pages+54852%E2%80%9354)
269. Tianfan Xue, Jianzhuang Liu, and Xiaoou Tang. 2010. Object cut: Complex 3d object reconstruction through line drawing separation. In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 1149–1156. IEEE. [find](https://scholar.google.com/scholar?q=Tianfan+Xue%2C+Jianzhuang+Liu%2C+and+Xiaoou+Tang%2E+2010%2E+Object+cut%3A+Complex+3d+object+reconstruction+through+line+drawing+separation%2E+In+2010+IEEE+Computer+Society+Conference+on+Computer+Vision+and+Patter)
270. Tianfan Xue, Jianzhuang Liu, and Xiaoou Tang. 2012. Example-based 3d object reconstruction from line drawings. In 2012 IEEE Conference on Computer Vision and Pattern Recognition, pages 302–309. IEEE. [find](https://scholar.google.com/scholar?q=Tianfan+Xue%2C+Jianzhuang+Liu%2C+and+Xiaoou+Tang%2E+2012%2E+Example%2Dbased+3d+object+reconstruction+from+line+drawings%2E+In+2012+IEEE+Conference+on+Computer+Vision+and+Pattern+Recognition%2C+pages+302%E2%80%93309%2E+IEEE%2E)
271. Kai Yan, Yufei Xu, Zhengyin Du, Xuesong Yao, Zheyu Wang, Xiaowen Guo, and Jiecao Chen. 2025. [Recitation over reasoning: How cutting-edge language models can fail on elementary school-level reasoning problems?](https://md.research.elamur.ai/free/en/papers/yan-2025-recitation-over-reasoning-how-cutting/) In Proceedings of the 14th International Joint Conference on Natural Language Processing an…
272. Yibo Yan, Jiamin Su, Jianxiang He, Fangteng Fu, Xu Zheng, Yuanhuiyi Lyu, Kun Wang, Shen Wang, Qingsong Wen, and Xuming Hu. 2024. A survey of mathematical reasoning in the era of multimodal large language model: Benchmark, method & challenges. arXiv preprint arXiv:2412.11936. [find](https://scholar.google.com/scholar?q=Yibo+Yan%2C+Jiamin+Su%2C+Jianxiang+He%2C+Fangteng+Fu%2C+Xu+Zheng%2C+Yuanhuiyi+Lyu%2C+Kun+Wang%2C+Shen+Wang%2C+Qingsong+Wen%2C+and+Xuming+Hu%2E+2024%2E+A+survey+of+mathematical+reasoning+in+the+era+of+multimodal+large+langu)
273. Bo Yang, Qingping Yang, Yingwei Ma, and Runtao Liu. 2025a. Utmath: A benchmark for math evaluation with unit test. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 5899–5915. [find](https://scholar.google.com/scholar?q=Bo+Yang%2C+Qingping+Yang%2C+Yingwei+Ma%2C+and+Runtao+Liu%2E+2025a%2E+Utmath%3A+A+benchmark+for+math+evaluation+with+unit+test%2E+In+Findings+of+the+Association+for+Computational+Linguistics%3A+EMNLP+2025%2C+pages+5899%E2%80%93)
274. Linjie Yang, Jianzhuang Liu, and Xiaoou Tang. 2013. Complex 3d general object reconstruction from line drawings. In Proceedings of the IEEE International Conference on Computer Vision, pages 1433–1440. [find](https://scholar.google.com/scholar?q=Linjie+Yang%2C+Jianzhuang+Liu%2C+and+Xiaoou+Tang%2E+2013%2E+Complex+3d+general+object+reconstruction+from+line+drawings%2E+In+Proceedings+of+the+IEEE+International+Conference+on+Computer+Vision%2C+pages+1433%E2%80%931440)
275. Yingxuan Yang, Bo Huang, Siyuan Qi, Chao Feng, Haoyi Hu, Yuxuan Zhu, Jinbo Hu, Haoran Zhao, Ziyi He, Xiao Liu, and 1 others. 2025b. Who’s the mvp? a game-theoretic evaluation benchmark for modular attribution in llm agents. arXiv preprint arXiv:2502.00510. — [arXiv:2502.00510](https://arxiv.org/abs/2502.00510) [find](https://scholar.google.com/scholar?q=Yingxuan+Yang%2C+Bo+Huang%2C+Siyuan+Qi%2C+Chao+Feng%2C+Haoyi+Hu%2C+Yuxuan+Zhu%2C+Jinbo+Hu%2C+Haoran+Zhao%2C+Ziyi+He%2C+Xiao+Liu%2C+and+1+others%2E+2025b%2E+Who%E2%80%99s+the+mvp%3F+a+game%2Dtheoretic+evaluation+benchmark+for+modular+att)
276. Yulong Yang, Hongguang Fu, Xiuqin Zhong, and Tianming Zhang. 2023. Suffi-gpsc: Sufficient geometry problem solution checking with symbolic computation and logical reasoning. In 2023 20th International Computer Conference on Wavelet Active Media Technology and Information Processing (IC-CWAMTIP), pag… [find](https://scholar.google.com/scholar?q=Yulong+Yang%2C+Hongguang+Fu%2C+Xiuqin+Zhong%2C+and+Tianming+Zhang%2E+2023%2E+Suffi%2Dgpsc%3A+Sufficient+geometry+problem+solution+checking+with+symbolic+computation+and+logical+reasoning%2E+In+2023+20th+International)
277. Zhen Yang, Jinhao Chen, Zhengxiao Du, Wenmeng Yu, Weihan Wang, Wenyi Hong, Zhihuan Jiang, Bin Xu, and Jie Tang. 2024. [Mathglm-vision: Solving mathematical problems with multi-modal large language model](https://md.research.elamur.ai/free/en/papers/yang-2024-mathglm-vision-solving-mathematical-problems/). arXiv preprint arXiv:2409.13729.
278. Huanjin Yao, Jiaxing Huang, Wenhao Wu, Jingyi Zhang, Yibo Wang, Shunyu Liu, Yingjie Wang, YuXin Song, Haocheng Feng, Li Shen, and 1 others. 2025. Mulberry: Empowering mllm with o1-like reasoning and reflection via collective monte carlo tree search. In The Thirty-ninth Annual Conference on Neural In… [find](https://scholar.google.com/scholar?q=Huanjin+Yao%2C+Jiaxing+Huang%2C+Wenhao+Wu%2C+Jingyi+Zhang%2C+Yibo+Wang%2C+Shunyu+Liu%2C+Yingjie+Wang%2C+YuXin+Song%2C+Haocheng+Feng%2C+Li+Shen%2C+and+1+others%2E+2025%2E+Mulberry%3A+Empowering+mllm+with+o1%2Dlike+reasoning+and+r)
279. Katherine Ye, Wode Ni, Max Krieger, Dor Ma’ayan, Jenna Wise, Jonathan Aldrich, Joshua Sunshine, and Keenan Crane. 2020. Penrose: from mathematical notation to beautiful diagrams. ACM Transactions on Graphics (TOG), 39(4):144–1. [find](https://scholar.google.com/scholar?q=Katherine+Ye%2C+Wode+Ni%2C+Max+Krieger%2C+Dor+Ma%E2%80%99ayan%2C+Jenna+Wise%2C+Jonathan+Aldrich%2C+Joshua+Sunshine%2C+and+Keenan+Crane%2E+2020%2E+Penrose%3A+from+mathematical+notation+to+beautiful+diagrams%2E+ACM+Transactions+on+G)
280. Zhangyue Yin, Qiushi Sun, Qipeng Guo, Zhiyuan Zeng, Qinyuan Cheng, Xipeng Qiu, and Xuan-Jing Huang. 2024. Explicit memory learning with expectation maximization. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 16618–16635. [find](https://scholar.google.com/scholar?q=Zhangyue+Yin%2C+Qiushi+Sun%2C+Qipeng+Guo%2C+Zhiyuan+Zeng%2C+Qinyuan+Cheng%2C+Xipeng+Qiu%2C+and+Xuan%2DJing+Huang%2E+2024%2E+Explicit+memory+learning+with+expectation+maximization%2E+In+Proceedings+of+the+2024+Conference+)
281. Alex Young, Bei Chen, Chao Li, Chengen Huang, Ge Zhang, Guanwei Zhang, Guoyin Wang, Heng Li, Jiangcheng Zhu, Jianqun Chen, and 1 others. 2024. [Yi: Open foundation models by 01](https://md.research.elamur.ai/free/en/papers/ai-2024-yi-open-foundation-models-01/). ai. arXiv preprint arXiv:2403.04652.
282. Wei Yu, Mengzhu Wang, Xiaodong Wang, Xun Zhou, Yongfu Zha, Yongjian Zhang, Shuyu Miao, and Jingdong Liu. 2021a. Geore: A relation extraction dataset for chinese geometry problems. In 35th Conference on Neural Information Processing Systems (NeurIPS 2021) Workshop on Math AI for Education (MATHAI4ED)… [find](https://scholar.google.com/scholar?q=Wei+Yu%2C+Mengzhu+Wang%2C+Xiaodong+Wang%2C+Xun+Zhou%2C+Yongfu+Zha%2C+Yongjian+Zhang%2C+Shuyu+Miao%2C+and+Jingdong+Liu%2E+2021a%2E+Geore%3A+A+relation+extraction+dataset+for+chinese+geometry+problems%2E+In+35th+Conference+o)
283. Xinguo Yu, Wenbin Gan, Danfeng Yang, and Sichao Lai. 2015. Automatic reconstruction of plane geometry figures in documents. In 2015 International Conference of Educational Innovation through Technology (EITT), pages 46–50. IEEE. [find](https://scholar.google.com/scholar?q=Xinguo+Yu%2C+Wenbin+Gan%2C+Danfeng+Yang%2C+and+Sichao+Lai%2E+2015%2E+Automatic+reconstruction+of+plane+geometry+figures+in+documents%2E+In+2015+International+Conference+of+Educational+Innovation+through+Technolog)
284. Xinguo Yu, Yixing Geng, and Zihan Feng. 2021b. Solving solid geometric calculation problems in text. In 2021 IEEE International Conference on Engineering, Technology & Education (TALE), pages 525–530. IEEE. [find](https://scholar.google.com/scholar?q=Xinguo+Yu%2C+Yixing+Geng%2C+and+Zihan+Feng%2E+2021b%2E+Solving+solid+geometric+calculation+problems+in+text%2E+In+2021+IEEE+International+Conference+on+Engineering%2C+Technology+%26+Education+%28TALE%29%2C+pages+525%E2%80%93530%2E)
285. Zhou Yu, Jun Yu, Yuhao Cui, Dacheng Tao, and Qi Tian. 2019. Deep modular co-attention networks for visual question answering. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6281–6290. [find](https://scholar.google.com/scholar?q=Zhou+Yu%2C+Jun+Yu%2C+Yuhao+Cui%2C+Dacheng+Tao%2C+and+Qi+Tian%2E+2019%2E+Deep+modular+co%2Dattention+networks+for+visual+question+answering%2E+In+Proceedings+of+the+IEEE%2FCVF+conference+on+computer+vision+and+pattern+r)
286. Albert S Yue, Lovish Madaan, Ted Moskovitz, DJ Strouse, and Aaditya K Singh. 2024a. Harp: A challenging human-annotated math reasoning benchmark. arXiv preprint arXiv:2412.08819. — [arXiv:2412.08819](https://arxiv.org/abs/2412.08819) [find](https://scholar.google.com/scholar?q=Albert+S+Yue%2C+Lovish+Madaan%2C+Ted+Moskovitz%2C+DJ+Strouse%2C+and+Aaditya+K+Singh%2E+2024a%2E+Harp%3A+A+challenging+human%2Dannotated+math+reasoning+benchmark%2E+arXiv+preprint+arXiv%3A2412%2E08819%2E+%E2%80%94+%5BarXiv%3A2412%2E08819%5D%28)
287. Xiang Yue, Tianyu Zheng, Ge Zhang, and Wenhu Chen. 2024b. [Mammoth2: Scaling instructions from the web](https://md.research.elamur.ai/free/en/papers/yue-2024-mammoth2-scaling-instructions-web/). Advances in Neural Information Processing Systems, 37:90629–90660.
288. YAO Yuxuan, Han Wu, Zhijiang Guo, Zhou Biyan, Jiahui Gao, Sichun Luo, Hanxu Hou, Xiaojin Fu, and Linqi Song. 2024. Learning from correctness without prompting makes llm efficient reasoner. In First Conference on Language Modeling. [find](https://scholar.google.com/scholar?q=YAO+Yuxuan%2C+Han+Wu%2C+Zhijiang+Guo%2C+Zhou+Biyan%2C+Jiahui+Gao%2C+Sichun+Luo%2C+Hanxu+Hou%2C+Xiaojin+Fu%2C+and+Linqi+Song%2E+2024%2E+Learning+from+correctness+without+prompting+makes+llm+efficient+reasoner%2E+In+First+Co)
289. Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. 2023. [Sigmoid loss for language image pre-training](https://md.research.elamur.ai/free/en/papers/zhai-2023-sigmoid-loss-language-image-pre/). In Proceedings of the IEEE/CVF international conference on computer vision, pages 11975–11986.
290. Chao Zhang, Jing Xiao, and Xiaoyang Fu. 2024a. Enhancing geometry problem solving with attention mechanism and super-resolution. In 2024 5th International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE), pages 112–115. IEEE. [find](https://scholar.google.com/scholar?q=Chao+Zhang%2C+Jing+Xiao%2C+and+Xiaoyang+Fu%2E+2024a%2E+Enhancing+geometry+problem+solving+with+attention+mechanism+and+super%2Dresolution%2E+In+2024+5th+International+Conference+on+Big+Data+%26+Artificial+Intellige)
291. Chi Zhang, Jiajun Song, Siyu Li, Yitao Liang, Yuxi Ma, Wei Wang, Yixin Zhu, and Song-Chun Zhu. 2026. Proposing and solving olympiad geometry with guided tree search. Nature Machine Intelligence, pages 1–12. [find](https://scholar.google.com/scholar?q=Chi+Zhang%2C+Jiajun+Song%2C+Siyu+Li%2C+Yitao+Liang%2C+Yuxi+Ma%2C+Wei+Wang%2C+Yixin+Zhu%2C+and+Song%2DChun+Zhu%2E+2026%2E+Proposing+and+solving+olympiad+geometry+with+guided+tree+search%2E+Nature+Machine+Intelligence%2C+pages)
292. Dongxiang Zhang. 2022. Deep learning in automatic math word problem solvers. In AI in Learning: Designing the Future, pages 233–246. Springer International Publishing Cham. [find](https://scholar.google.com/scholar?q=Dongxiang+Zhang%2E+2022%2E+Deep+learning+in+automatic+math+word+problem+solvers%2E+In+AI+in+Learning%3A+Designing+the+Future%2C+pages+233%E2%80%93246%2E+Springer+International+Publishing+Cham%2E)
293. Jiarui Zhang, Ollie Liu, Tianyu Yu, Jinyi Hu, and Willie Neiswanger. 2025a. Euclid: Supercharging multimodal LLMs with synthetic high-fidelity visual descriptions. In Will Synthetic Data Finally Solve the Data Access Problem? [find](https://scholar.google.com/scholar?q=Jiarui+Zhang%2C+Ollie+Liu%2C+Tianyu+Yu%2C+Jinyi+Hu%2C+and+Willie+Neiswanger%2E+2025a%2E+Euclid%3A+Supercharging+multimodal+LLMs+with+synthetic+high%2Dfidelity+visual+descriptions%2E+In+Will+Synthetic+Data+Finally+Solve)
294. Jiaxin Zhang, Yinghui Jiang, and Yashar Moshfeghi. 2024b. Gaps: geometry-aware problem solver. arXiv preprint arXiv:2401.16287. — [arXiv:2401.16287](https://arxiv.org/abs/2401.16287) [find](https://scholar.google.com/scholar?q=Jiaxin+Zhang%2C+Yinghui+Jiang%2C+and+Yashar+Moshfeghi%2E+2024b%2E+Gaps%3A+geometry%2Daware+problem+solver%2E+arXiv+preprint+arXiv%3A2401%2E16287%2E+%E2%80%94+%5BarXiv%3A2401%2E16287%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2401%2E16287%29)
295. Jiaxin Zhang, Zhong-Zhi Li, Ming-Liang Zhang, Fei Yin, Cheng-Lin Liu, and Yashar Moshfeghi. 2024c. [find](https://scholar.google.com/scholar?q=Jiaxin+Zhang%2C+Zhong%2DZhi+Li%2C+Ming%2DLiang+Zhang%2C+Fei+Yin%2C+Cheng%2DLin+Liu%2C+and+Yashar+Moshfeghi%2E+2024c%2E)
296. Geoeval: Benchmark for evaluating llms and multimodal models on geometry problem-solving. In Findings of the Association for Computational Linguistics ACL 2024, pages 1258–1276. [find](https://scholar.google.com/scholar?q=Geoeval%3A+Benchmark+for+evaluating+llms+and+multimodal+models+on+geometry+problem%2Dsolving%2E+In+Findings+of+the+Association+for+Computational+Linguistics+ACL+2024%2C+pages+1258%E2%80%931276%2E)
297. Hengel, and Yuan Xue. 2025e. Open eyes, then reason: Fine-grained visual mathematical understanding in mllms. arXiv preprint arXiv:2501.06430. — [arXiv:2501.06430](https://arxiv.org/abs/2501.06430) [find](https://scholar.google.com/scholar?q=Hengel%2C+and+Yuan+Xue%2E+2025e%2E+Open+eyes%2C+then+reason%3A+Fine%2Dgrained+visual+mathematical+understanding+in+mllms%2E+arXiv+preprint+arXiv%3A2501%2E06430%2E+%E2%80%94+%5BarXiv%3A2501%2E06430%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2501%2E06430%29)
298. Jiaxin Zhang and Yashar Moshfeghi. 2024. Gold: Geometry problem solver with natural language description. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 263–278. [find](https://scholar.google.com/scholar?q=Jiaxin+Zhang+and+Yashar+Moshfeghi%2E+2024%2E+Gold%3A+Geometry+problem+solver+with+natural+language+description%2E+In+Findings+of+the+Association+for+Computational+Linguistics%3A+NAACL+2024%2C+pages+263%E2%80%93278%2E)
299. Wenxuan Zhang, Mahani Aljunied, Chang Gao, Yew Ken Chia, and Lidong Bing. 2023b. M3exam: A multilingual, multimodal, multilevel benchmark for examining large language models. Advances in Neural Information Processing Systems, 36:5484–5505. [find](https://scholar.google.com/scholar?q=Wenxuan+Zhang%2C+Mahani+Aljunied%2C+Chang+Gao%2C+Yew+Ken+Chia%2C+and+Lidong+Bing%2E+2023b%2E+M3exam%3A+A+multilingual%2C+multimodal%2C+multilevel+benchmark+for+examining+large+language+models%2E+Advances+in+Neural+Inform)
300. Lunjun Zhang, Arian Hosseini, Hritik Bansal, Mehran Kazemi, Aviral Kumar, and Rishabh Agarwal. 2025b. [Generative verifiers: Reward modeling as next-token prediction](https://md.research.elamur.ai/free/en/papers/zhang-2024-generative-verifiers-reward-modeling-as/). In The Thirteenth International Conference on Learning Representations, ICLR 2025, Singapore, April 24-28, 2025. OpenReview.net.
301. Xiaokai Zhang, Yang Li, Na Zhu, Cheng Qin, Zhenbing Zeng, and Tuo Leng. 2025f. Fgeo-hypergnet: geometric problem solving integrating formalgeo symbolic system and hypergraph neural network. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, pages 4733–4741… [find](https://scholar.google.com/scholar?q=Xiaokai+Zhang%2C+Yang+Li%2C+Na+Zhu%2C+Cheng+Qin%2C+Zhenbing+Zeng%2C+and+Tuo+Leng%2E+2025f%2E+Fgeo%2Dhypergnet%3A+geometric+problem+solving+integrating+formalgeo+symbolic+system+and+hypergraph+neural+network%2E+In+Proceed)
302. Ming-Liang Zhang, Zhong-Zhi Li, Fei Yin, Liang Lin, and Cheng-Lin Liu. 2024d. Fuse, reason and verify: Geometry problem solving with parsed clauses from diagram. arXiv preprint arXiv:2407.07327. — [arXiv:2407.07327](https://arxiv.org/abs/2407.07327) [find](https://scholar.google.com/scholar?q=Ming%2DLiang+Zhang%2C+Zhong%2DZhi+Li%2C+Fei+Yin%2C+Liang+Lin%2C+and+Cheng%2DLin+Liu%2E+2024d%2E+Fuse%2C+reason+and+verify%3A+Geometry+problem+solving+with+parsed+clauses+from+diagram%2E+arXiv+preprint+arXiv%3A2407%2E07327%2E+%E2%80%94+%5Bar)
303. Xiaokai Zhang, Na Zhu, Yiming He, Jia Zou, Qike Huang, Xiaoxiao Jin, Yanjun Guo, Chenyang Mao, Yang Li, Zhe Zhu, and 1 others. 2023c. Formalgeo: An extensible formalized framework for olympiad geometric problem solving. arXiv preprint arXiv:2310.18021. — [arXiv:2310.18021](https://arxiv.org/abs/2310.18021) [find](https://scholar.google.com/scholar?q=Xiaokai+Zhang%2C+Na+Zhu%2C+Yiming+He%2C+Jia+Zou%2C+Qike+Huang%2C+Xiaoxiao+Jin%2C+Yanjun+Guo%2C+Chenyang+Mao%2C+Yang+Li%2C+Zhe+Zhu%2C+and+1+others%2E+2023c%2E+Formalgeo%3A+An+extensible+formalized+framework+for+olympiad+geometr)
304. Ming-Liang Zhang, Fei Yin, Yi-Han Hao, and Cheng-Lin Liu. 2022a. Learning to understand plane geometry diagram [c]. In 36th Conference on Neural Information Processing Systems (NeurIPS) Workshop on MATH-AI. [find](https://scholar.google.com/scholar?q=Ming%2DLiang+Zhang%2C+Fei+Yin%2C+Yi%2DHan+Hao%2C+and+Cheng%2DLin+Liu%2E+2022a%2E+Learning+to+understand+plane+geometry+diagram+%5Bc%5D%2E+In+36th+Conference+on+Neural+Information+Processing+Systems+%28NeurIPS%29+Workshop+on+MA)
305. Ming-Liang Zhang, Fei Yin, Yi-Han Hao, and Cheng-Lin Liu. 2022b. Plane geometry diagram parsing. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, pages 1636– 1643. International Joint Conferences on Artificial Intelligence Organization. [find](https://scholar.google.com/scholar?q=Ming%2DLiang+Zhang%2C+Fei+Yin%2C+Yi%2DHan+Hao%2C+and+Cheng%2DLin+Liu%2E+2022b%2E+Plane+geometry+diagram+parsing%2E+In+Proceedings+of+the+Thirty%2DFirst+International+Joint+Conference+on+Artificial+Intelligence%2C+pages+163)
306. Ming-Liang Zhang, Fei Yin, and Cheng-Lin Liu. 2023a. A multi-modal neural geometric solver with textual clauses parsed from diagram. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, pages 3374–3382. [find](https://scholar.google.com/scholar?q=Ming%2DLiang+Zhang%2C+Fei+Yin%2C+and+Cheng%2DLin+Liu%2E+2023a%2E+A+multi%2Dmodal+neural+geometric+solver+with+textual+clauses+parsed+from+diagram%2E+In+Proceedings+of+the+Thirty%2DSecond+International+Joint+Conference+)
307. Qiyuan Zhang, Fuyuan Lyu, Zexu Sun, Lei Wang, Weixu Zhang, Wenyue Hua, Haolun Wu, Zhihan Guo, Yufei Wang, Niklas Muennighoff, and 1 others. 2025c. A survey on test-time scaling in large language models: What, how, where, and how well? arXiv preprint arXiv:2503.24235. [find](https://scholar.google.com/scholar?q=Qiyuan+Zhang%2C+Fuyuan+Lyu%2C+Zexu+Sun%2C+Lei+Wang%2C+Weixu+Zhang%2C+Wenyue+Hua%2C+Haolun+Wu%2C+Zhihan+Guo%2C+Yufei+Wang%2C+Niklas+Muennighoff%2C+and+1+others%2E+2025c%2E+A+survey+on+test%2Dtime+scaling+in+large+language+model)
308. Renrui Zhang, Dongzhi Jiang, Yichi Zhang, Haokun Lin, Ziyu Guo, Pengshuo Qiu, Aojun Zhou, Pan Lu, Kai-Wei Chang, Yu Qiao, and 1 others. 2024e. [Mathverse: Does your multi-modal llm truly see the diagrams in visual math problems?](https://md.research.elamur.ai/free/en/papers/zhang-2024-mathverse-multi-modal-llm-truly/) In European Conference on Computer Vision, pages 169–186. Springer.
309. Renrui Zhang, Xinyu Wei, Dongzhi Jiang, Ziyu Guo, Yichi Zhang, Chengzhuo Tong, Jiaming Liu, Aojun Zhou, Shanghang Zhang, Peng Gao, and Hongsheng Li. 2025d. MAVIS: Mathematical visual instruction tuning with an automatic data engine. In The Thirteenth International Conference on Learning Representati… [find](https://scholar.google.com/scholar?q=Renrui+Zhang%2C+Xinyu+Wei%2C+Dongzhi+Jiang%2C+Ziyu+Guo%2C+Yichi+Zhang%2C+Chengzhuo+Tong%2C+Jiaming+Liu%2C+Aojun+Zhou%2C+Shanghang+Zhang%2C+Peng+Gao%2C+and+Hongsheng+Li%2E+2025d%2E+MAVIS%3A+Mathematical+visual+instruction+tunin)
310. Shan Zhang, Aotian Chen, Yanpeng Sun, Jindong Gu, Yi-Yu Zheng, Piotr Koniusz, Kai Zou, Anton van den Xiaokai Zhang, Na Zhu, Cheng Qin, LI Yang, Zhenbing Zeng, and Tuo Leng. 2024f. Formal representation and solution of plane geometric problems. In The 4th Workshop on Mathematical Reasoning and AI at… [find](https://scholar.google.com/scholar?q=Shan+Zhang%2C+Aotian+Chen%2C+Yanpeng+Sun%2C+Jindong+Gu%2C+Yi%2DYu+Zheng%2C+Piotr+Koniusz%2C+Kai+Zou%2C+Anton+van+den+Xiaokai+Zhang%2C+Na+Zhu%2C+Cheng+Qin%2C+LI+Yang%2C+Zhenbing+Zeng%2C+and+Tuo+Leng%2E+2024f%2E+Formal+representatio)
311. Yifan Zhang, Jingqin Yang, Yang Yuan, and Andrew C Yao. 2025g. Cumulative reasoning with large language models. Transactions on Machine Learning Research. [find](https://scholar.google.com/scholar?q=Yifan+Zhang%2C+Jingqin+Yang%2C+Yang+Yuan%2C+and+Andrew+C+Yao%2E+2025g%2E+Cumulative+reasoning+with+large+language+models%2E+Transactions+on+Machine+Learning+Research%2E)
312. Zeren Zhang, Jo-Ku Cheng, Jingyang Deng, Lu Tian, Jinwen Ma, Ziran Qin, Xiaokai Zhang, Na Zhu, and Tuo Leng. 2025h. Diagram formalization enhanced multi-modal geometry problem solver. In ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1–5. IE… [find](https://scholar.google.com/scholar?q=Zeren+Zhang%2C+Jo%2DKu+Cheng%2C+Jingyang+Deng%2C+Lu+Tian%2C+Jinwen+Ma%2C+Ziran+Qin%2C+Xiaokai+Zhang%2C+Na+Zhu%2C+and+Tuo+Leng%2E+2025h%2E+Diagram+formalization+enhanced+multi%2Dmodal+geometry+problem+solver%2E+In+ICASSP+2025%2D2)
313. Junbo Zhao, Ting Zhang, Jiayu Sun, Mi Tian, and Hua Huang. 2025a. Pi-gps: Enhancing geometry problem solving by unleashing the power of diagrammatic information. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1526–1536. [find](https://scholar.google.com/scholar?q=Junbo+Zhao%2C+Ting+Zhang%2C+Jiayu+Sun%2C+Mi+Tian%2C+and+Hua+Huang%2E+2025a%2E+Pi%2Dgps%3A+Enhancing+geometry+problem+solving+by+unleashing+the+power+of+diagrammatic+information%2E+In+Proceedings+of+the+IEEE%2FCVF+Interna)
314. Xueliang Zhao, Xinting Huang, Tingchen Fu, Qintong Li, Shansan Gong, Lemao Liu, Wei Bi, and Lingpeng Kong. 2024. Bba: Bi-modal behavioral alignment for reasoning with large vision-language models. In Findings of the Association for Computational Linguistics ACL 2024, pages 7255–7279. [find](https://scholar.google.com/scholar?q=Xueliang+Zhao%2C+Xinting+Huang%2C+Tingchen+Fu%2C+Qintong+Li%2C+Shansan+Gong%2C+Lemao+Liu%2C+Wei+Bi%2C+and+Lingpeng+Kong%2E+2024%2E+Bba%3A+Bi%2Dmodal+behavioral+alignment+for+reasoning+with+large+vision%2Dlanguage+models%2E+In+)
315. Yurui Zhao, Xiang Wang, Jiahong Liu, Irwin King, and Zhitao Huang. 2025b. Towards geometry problem solving in the large model era: A survey. In 2nd AI for Math Workshop@ ICML 2025. [find](https://scholar.google.com/scholar?q=Yurui+Zhao%2C+Xiang+Wang%2C+Jiahong+Liu%2C+Irwin+King%2C+and+Zhitao+Huang%2E+2025b%2E+Towards+geometry+problem+solving+in+the+large+model+era%3A+A+survey%2E+In+2nd+AI+for+Math+Workshop%40+ICML+2025%2E)
316. Chuanyang Zheng, Zhengying Liu, Enze Xie, Zhenguo Li, and Yu Li. 2024. Progressive-hint prompting improves reasoning in large language models. In AI for Math Workshop@ ICML 2024. [find](https://scholar.google.com/scholar?q=Chuanyang+Zheng%2C+Zhengying+Liu%2C+Enze+Xie%2C+Zhenguo+Li%2C+and+Yu+Li%2E+2024%2E+Progressive%2Dhint+prompting+improves+reasoning+in+large+language+models%2E+In+AI+for+Math+Workshop%40+ICML+2024%2E)
317. Jinxin Zheng, Yongtao Wang, and Zhi Tang. 2015. Solid geometric object reconstruction from single line drawing image. In International Conference on Computer Graphics Theory and Applications, volume 2, pages 391–400. SCITEPRESS. [find](https://scholar.google.com/scholar?q=Jinxin+Zheng%2C+Yongtao+Wang%2C+and+Zhi+Tang%2E+2015%2E+Solid+geometric+object+reconstruction+from+single+line+drawing+image%2E+In+International+Conference+on+Computer+Graphics+Theory+and+Applications%2C+volume+2)
318. Wenwen Zhuang, Xin Huang, Xiantao Zhang, and Jin Zeng. 2025. Math-puma: Progressive upward multimodal alignment to enhance mathematical reasoning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pages 26183–26191. [find](https://scholar.google.com/scholar?q=Wenwen+Zhuang%2C+Xin+Huang%2C+Xiantao+Zhang%2C+and+Jin+Zeng%2E+2025%2E+Math%2Dpuma%3A+Progressive+upward+multimodal+alignment+to+enhance+mathematical+reasoning%2E+In+Proceedings+of+the+AAAI+Conference+on+Artificial+I)
319. Jinxin Zheng, Yongtao Wang, and Zhi Tang. 2016a. Context-aware geometric object reconstruction for mobile education. In Proceedings of the 24th ACM international conference on Multimedia, pages 367– 371. [find](https://scholar.google.com/scholar?q=Jinxin+Zheng%2C+Yongtao+Wang%2C+and+Zhi+Tang%2E+2016a%2E+Context%2Daware+geometric+object+reconstruction+for+mobile+education%2E+In+Proceedings+of+the+24th+ACM+international+conference+on+Multimedia%2C+pages+367%E2%80%93+3)
320. Changqing Zou, Tianfan Xue, Xiaojiang Peng, Honghua Li, Baochang Zhang, Ping Tan, and Jianzhuang Liu. 2016. An example-based approach to 3d man-made object reconstruction from line drawings. Pattern Recognition, 60:543–553. [find](https://scholar.google.com/scholar?q=Changqing+Zou%2C+Tianfan+Xue%2C+Xiaojiang+Peng%2C+Honghua+Li%2C+Baochang+Zhang%2C+Ping+Tan%2C+and+Jianzhuang+Liu%2E+2016%2E+An+example%2Dbased+approach+to+3d+man%2Dmade+object+reconstruction+from+line+drawings%2E+Pattern+R)
321. Jinxin Zheng, Yongtao Wang, and Zhi Tang. 2016b. Recovering solid geometric object from single line drawing image. Multimedia Tools and Applications, 75:10153–10174. [find](https://scholar.google.com/scholar?q=Jinxin+Zheng%2C+Yongtao+Wang%2C+and+Zhi+Tang%2E+2016b%2E+Recovering+solid+geometric+object+from+single+line+drawing+image%2E+Multimedia+Tools+and+Applications%2C+75%3A10153%E2%80%9310174%2E)
322. Chengke Zou, Xingang Guo, Rui Yang, Junyu Zhang, Bin Hu, and Huan Zhang. 2025. Dynamath: A dynamic visual benchmark for evaluating mathematical reasoning robustness of vision language models. In The Thirteenth International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Chengke+Zou%2C+Xingang+Guo%2C+Rui+Yang%2C+Junyu+Zhang%2C+Bin+Hu%2C+and+Huan+Zhang%2E+2025%2E+Dynamath%3A+A+dynamic+visual+benchmark+for+evaluating+mathematical+reasoning+robustness+of+vision+language+models%2E+In+The+T)
323. Hu Zhengyu and Zhong Xiuqin. 2023. A precise text-to-diagram generation method for elementary geometry. In 2023 20th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), pages 1–7. IEEE. [find](https://scholar.google.com/scholar?q=Hu+Zhengyu+and+Zhong+Xiuqin%2E+2023%2E+A+precise+text%2Dto%2Ddiagram+generation+method+for+elementary+geometry%2E+In+2023+20th+International+Computer+Conference+on+Wavelet+Active+Media+Technology+and+Informatio)
324. Junjie Zhou, Zheng Liu, Shitao Xiao, Bo Zhao, and Yongping Xiong. 2024a. Vista: Visualized text embedding for universal multi-modal retrieval. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3185–3200. [find](https://scholar.google.com/scholar?q=Junjie+Zhou%2C+Zheng+Liu%2C+Shitao+Xiao%2C+Bo+Zhao%2C+and+Yongping+Xiong%2E+2024a%2E+Vista%3A+Visualized+text+embedding+for+universal+multi%2Dmodal+retrieval%2E+In+Proceedings+of+the+62nd+Annual+Meeting+of+the+Associat)
325. Jia Zou, Xiaokai Zhang, Yiming He, Na Zhu, and Tuo Leng. 2024. Fgeo-drl: Deductive reasoning for geometric problems through deep reinforcement learning. Symmetry, 16(4):437. [find](https://scholar.google.com/scholar?q=Jia+Zou%2C+Xiaokai+Zhang%2C+Yiming+He%2C+Na+Zhu%2C+and+Tuo+Leng%2E+2024%2E+Fgeo%2Ddrl%3A+Deductive+reasoning+for+geometric+problems+through+deep+reinforcement+learning%2E+Symmetry%2C+16%284%29%3A437%2E)
326. Mingrui Zhou and Xinguo Yu. 2021. Proving geometric problem by adding auxiliary lines-based on hypothetical test. In International Conference on Artificial Intelligence in Education Technology, pages 151–161. Springer. [find](https://scholar.google.com/scholar?q=Mingrui+Zhou+and+Xinguo+Yu%2E+2021%2E+Proving+geometric+problem+by+adding+auxiliary+lines%2Dbased+on+hypothetical+test%2E+In+International+Conference+on+Artificial+Intelligence+in+Education+Technology%2C+pages+)
327. Minxuan Zhou, Hao Liang, Tianpeng Li, Zhiyu Wu, Mingan Lin, Linzhuang Sun, Yaqi Zhou, Yan Zhang, Xiaoqin Huang, Yicong Chen, and 1 others. 2024b. Mathscape: Evaluating mllms in multimodal math scenarios through a hierarchical benchmark. arXiv preprint arXiv:2408.07543. — [arXiv:2408.07543](https://arxiv.org/abs/2408.07543) [find](https://scholar.google.com/scholar?q=Minxuan+Zhou%2C+Hao+Liang%2C+Tianpeng+Li%2C+Zhiyu+Wu%2C+Mingan+Lin%2C+Linzhuang+Sun%2C+Yaqi+Zhou%2C+Yan+Zhang%2C+Xiaoqin+Huang%2C+Yicong+Chen%2C+and+1+others%2E+2024b%2E+Mathscape%3A+Evaluating+mllms+in+multimodal+math+scenari)
328. Motian Zhou, Chen Wu, and Chao Sun. 2024c. Evaluating automated geometric problem solving with formal language generation on large multimodal models. In 2024 International Conference on Intelligent Education and Intelligent Research (IEIR), pages 1–7. IEEE. [find](https://scholar.google.com/scholar?q=Motian+Zhou%2C+Chen+Wu%2C+and+Chao+Sun%2E+2024c%2E+Evaluating+automated+geometric+problem+solving+with+formal+language+generation+on+large+multimodal+models%2E+In+2024+International+Conference+on+Intelligent+Ed)
329. Wei Zhou, Ruixi Xu, Hao Guan, Jietong Zhao, and Yongsheng Rao. 2022. Research on geometry problem text understanding based on bidirectional lstm-crf. In 2022 9th International Conference on Digital Home (ICDH), pages 121–127. IEEE. [find](https://scholar.google.com/scholar?q=Wei+Zhou%2C+Ruixi+Xu%2C+Hao+Guan%2C+Jietong+Zhao%2C+and+Yongsheng+Rao%2E+2022%2E+Research+on+geometry+problem+text+understanding+based+on+bidirectional+lstm%2Dcrf%2E+In+2022+9th+International+Conference+on+Digital+Ho)
330. Zihao Zhou, Shudong Liu, Maizhen Ning, Wei Liu, Jindong Wang, Derek F Wong, Xiaowei Huang, Qiufeng Wang, and Kaizhu Huang. 2025. Is your model really a good math reasoner? evaluating mathematical reasoning with checklist. In The Thirteenth International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Zihao+Zhou%2C+Shudong+Liu%2C+Maizhen+Ning%2C+Wei+Liu%2C+Jindong+Wang%2C+Derek+F+Wong%2C+Xiaowei+Huang%2C+Qiufeng+Wang%2C+and+Kaizhu+Huang%2E+2025%2E+Is+your+model+really+a+good+math+reasoner%3F+evaluating+mathematical+reas)
331. Na Zhu, Xiaokai Zhang, Qike Huang, Fangzhen Zhu, Zhenbing Zeng, and Tuo Leng. 2025. Fgeo-parser: Autoformalization and solution of plane geometric problems. Symmetry, 17(1):8. *Figure 5: Papers on deep learning for geometry problem solving over the years (data for 2025 is up to April).* [find](https://scholar.google.com/scholar?q=Na+Zhu%2C+Xiaokai+Zhang%2C+Qike+Huang%2C+Fangzhen+Zhu%2C+Zhenbing+Zeng%2C+and+Tuo+Leng%2E+2025%2E+Fgeo%2Dparser%3A+Autoformalization+and+solution+of+plane+geometric+problems%2E+Symmetry%2C+17%281%29%3A8%2E+%2AFigure+5%3A+Papers+on+dee)

*Updated 1 November 2026*